Cleaned up scripts, added more comments
This commit is contained in:
parent
e42f5bba1b
commit
a59f84b446
11 changed files with 103 additions and 436 deletions
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@ -1,5 +1,4 @@
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import gym
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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@ -9,10 +8,10 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import StochasticSelector
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from tensorboardX import SummaryWriter
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import torch.multiprocessing as mp
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import signal
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from copy import deepcopy
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#
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## Networks
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#
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class Value(nn.Module):
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def __init__(self, state_size):
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super(Value, self).__init__()
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@ -28,11 +27,8 @@ class Value(nn.Module):
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = self.fc3(x)
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return x
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class Policy(nn.Module):
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@ -48,50 +44,30 @@ class Policy(nn.Module):
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self.fc2_norm = nn.LayerNorm(64)
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self.fc3 = rn.NoisyLinear(64, action_size)
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# self.fc3_norm = nn.LayerNorm(action_size)
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# self.value_fc = rn.NoisyLinear(64, 64)
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# self.value_fc_norm = nn.LayerNorm(64)
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# self.value = rn.NoisyLinear(64, 1)
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# self.advantage_fc = rn.NoisyLinear(64, 64)
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# self.advantage_fc_norm = nn.LayerNorm(64)
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# self.advantage = rn.NoisyLinear(64, action_size)
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = F.softmax(self.fc3(x), dim = 1)
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# state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
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# state_value = self.value(state_value)
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# advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
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# advantage = self.advantage(advantage)
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# x = F.softmax(state_value + advantage - advantage.mean(), dim = 1)
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return x
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#
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## Configuration
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#
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config = {}
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config['seed'] = 901
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config['environment_name'] = 'Acrobot-v1'
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config['memory_size'] = 2000
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config['total_training_episodes'] = 500
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config['total_evaluation_episodes'] = 10
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config['batch_size'] = 32
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config['learning_rate'] = 1e-3
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config['target_sync_tau'] = 1e-1
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config['discount_rate'] = 0.99
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config['replay_skip'] = 0
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# How many episodes between printing out the episode stats
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config['print_stat_n_eps'] = 1
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config['disable_cuda'] = False
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#
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## Training Loop
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#
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def train(runner, agent, config, logger = None, logwriter = None):
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finished = False
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while not finished:
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@ -103,9 +79,8 @@ def train(runner, agent, config, logger = None, logwriter = None):
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logwriter.write(logger)
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finished = runner.episode_num > config['total_training_episodes']
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if __name__ == "__main__":
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torch.multiprocessing.set_sharing_strategy('file_system') # To not hit file descriptor memory limit
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if __name__ == "__main__":
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# Setting up the environment
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rltorch.set_seed(config['seed'])
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print("Setting up environment...", end = " ")
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@ -135,7 +110,6 @@ if __name__ == "__main__":
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actor = StochasticSelector(policy_net, action_size, memory, device = device)
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# Agent is what performs the training
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# agent = rltorch.agents.REINFORCEAgent(net, memory, config, target_net = target_net, logger = logger)
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agent = rltorch.agents.A2CSingleAgent(policy_net, value_net, memory, config, logger = logger)
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# Runner performs one episode in the environment
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@ -1,5 +1,4 @@
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import gym
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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@ -10,8 +9,10 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import StochasticSelector
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from tensorboardX import SummaryWriter
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import torch.multiprocessing as mp
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#
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## Networks
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#
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class Policy(nn.Module):
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def __init__(self, state_size, action_size):
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super(Policy, self).__init__()
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@ -32,37 +33,37 @@ class Policy(nn.Module):
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x = F.softmax(self.action_prob(x), dim = 1)
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return x
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#
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## Configuration
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#
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config = {}
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config['seed'] = 901
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config['environment_name'] = 'Acrobot-v1'
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config['memory_size'] = 2000
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config['total_training_episodes'] = 50
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config['total_evaluation_episodes'] = 5
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config['batch_size'] = 32
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config['learning_rate'] = 1e-1
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config['target_sync_tau'] = 1e-1
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config['discount_rate'] = 0.99
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config['replay_skip'] = 0
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# How many episodes between printing out the episode stats
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config['print_stat_n_eps'] = 1
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config['disable_cuda'] = False
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def train(env, net, actor, config, logger = None, logwriter = None):
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#
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## Training Loop
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#
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def train(runner, net, config, logger = None, logwriter = None):
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finished = False
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episode_num = 1
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while not finished:
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rltorch.env.simulateEnvEps(env, actor, config, logger = logger, name = "Training")
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episode_num += 1
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runner.run()
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net.calc_gradients()
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net.step()
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# When the episode number changes, log network paramters
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if logwriter is not None:
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net.log_named_parameters()
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logwriter.write(logger)
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finished = episode_num > config['total_training_episodes']
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finished = runner.episode_num > config['total_training_episodes']
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#
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## Loss function
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#
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def fitness(model):
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env = gym.make("Acrobot-v1")
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state = torch.from_numpy(env.reset()).float().unsqueeze(0)
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@ -75,9 +76,12 @@ def fitness(model):
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next_state, reward, done, _ = env.step(action)
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total_reward += reward
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state = torch.from_numpy(next_state).float().unsqueeze(0)
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return total_reward
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return -total_reward
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if __name__ == "__main__":
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# Hide internal gym warnings
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gym.logger.set_level(40)
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# Setting up the environment
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rltorch.set_seed(config['seed'])
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print("Setting up environment...", end = " ")
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@ -90,21 +94,21 @@ if __name__ == "__main__":
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# Logging
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logger = rltorch.log.Logger()
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# logwriter = rltorch.log.LogWriter(logger, SummaryWriter())
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logwriter = rltorch.log.LogWriter(SummaryWriter())
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# Setting up the networks
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device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
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net = rn.ESNetwork(Policy(state_size, action_size),
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torch.optim.Adam, 100, fitness, config, device = device, name = "ES", logger = logger)
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net.model.share_memory()
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# Actor takes a net and uses it to produce actions from given states
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actor = StochasticSelector(net, action_size, device = device)
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print("Training...")
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# Runner performs an episode of the environment
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runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name = "Training", logwriter = logwriter)
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train(env, net, actor, config, logger = logger, logwriter = logwriter)
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print("Training...")
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train(runner, net, config, logger = logger, logwriter = logwriter)
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# For profiling...
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# import cProfile
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@ -1,5 +1,4 @@
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import gym
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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@ -9,10 +8,10 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import StochasticSelector
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from tensorboardX import SummaryWriter
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import torch.multiprocessing as mp
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import signal
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from copy import deepcopy
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#
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## Networks
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#
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class Value(nn.Module):
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def __init__(self, state_size):
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super(Value, self).__init__()
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@ -28,11 +27,8 @@ class Value(nn.Module):
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = self.fc3(x)
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return x
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class Policy(nn.Module):
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self.fc2_norm = nn.LayerNorm(64)
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self.fc3 = rn.NoisyLinear(64, action_size)
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# self.fc3_norm = nn.LayerNorm(action_size)
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# self.value_fc = rn.NoisyLinear(64, 64)
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# self.value_fc_norm = nn.LayerNorm(64)
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# self.value = rn.NoisyLinear(64, 1)
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# self.advantage_fc = rn.NoisyLinear(64, 64)
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# self.advantage_fc_norm = nn.LayerNorm(64)
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# self.advantage = rn.NoisyLinear(64, action_size)
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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x = F.relu(self.fc2_norm(self.fc2(x)))
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x = F.softmax(self.fc3(x), dim = 1)
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# state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
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# state_value = self.value(state_value)
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# advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
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# advantage = self.advantage(advantage)
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# x = F.softmax(state_value + advantage - advantage.mean(), dim = 1)
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return x
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#
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## Configuration
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#
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config = {}
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config['seed'] = 901
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config['environment_name'] = 'Acrobot-v1'
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config['memory_size'] = 2000
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config['total_training_episodes'] = 500
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config['total_evaluation_episodes'] = 10
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config['batch_size'] = 32
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config['learning_rate'] = 1e-3
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config['target_sync_tau'] = 1e-1
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config['discount_rate'] = 0.99
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config['replay_skip'] = 0
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# How many episodes between printing out the episode stats
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config['print_stat_n_eps'] = 1
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config['disable_cuda'] = False
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#
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## Training Loop
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#
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def train(runner, agent, config, logger = None, logwriter = None):
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finished = False
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while not finished:
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@ -133,7 +109,6 @@ if __name__ == "__main__":
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actor = StochasticSelector(policy_net, action_size, memory, device = device)
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# Agent is what performs the training
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# agent = rltorch.agents.REINFORCEAgent(net, memory, config, target_net = target_net, logger = logger)
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agent = rltorch.agents.PPOAgent(policy_net, value_net, memory, config, logger = logger)
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# Runner performs a certain number of steps in the environment
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import gym
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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@ -9,9 +8,11 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import StochasticSelector
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from tensorboardX import SummaryWriter
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import torch.multiprocessing as mp
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from copy import deepcopy
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#
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## Networks
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#
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class Value(nn.Module):
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def __init__(self, state_size, action_size):
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super(Value, self).__init__()
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@ -39,7 +40,6 @@ class Value(nn.Module):
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advantage = self.advantage(advantage)
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x = state_value + advantage - advantage.mean()
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return x
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@ -63,6 +63,9 @@ class Policy(nn.Module):
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x = F.softmax(self.action_prob(x), dim = 1)
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return x
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#
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## Configuration
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#
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config = {}
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config['seed'] = 901
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config['environment_name'] = 'Acrobot-v1'
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@ -88,7 +91,9 @@ config['prioritized_replay_sampling_priority'] = 0.6
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config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)
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#
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## Training Loop
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#
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def train(runner, agent, config, logger = None, logwriter = None):
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finished = False
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last_episode_num = 1
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logwriter.write(logger)
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finished = runner.episode_num > config['total_training_episodes']
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if __name__ == "__main__":
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# Setting up the environment
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rltorch.set_seed(config['seed'])
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# Logging
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logger = rltorch.log.Logger()
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# logwriter = rltorch.log.LogWriter(logger, SummaryWriter())
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logwriter = rltorch.log.LogWriter(SummaryWriter())
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# Setting up the networks
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torch.optim.Adam, 500, None, config2, sigma = 0.1, device = device, name = "ES", logger = logger)
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value_net = rn.Network(Value(state_size, action_size),
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torch.optim.Adam, config, device = device, name = "DQN", logger = logger)
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target_net = rn.TargetNetwork(value_net, device = device)
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value_net.model.share_memory()
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target_net.model.share_memory()
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# Actor takes a net and uses it to produce actions from given states
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actor = StochasticSelector(policy_net, action_size, device = device)
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# Memory stores experiences for later training
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memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
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@ -141,11 +144,9 @@ if __name__ == "__main__":
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runner = rltorch.env.EnvironmentRunSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
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# Agent is what performs the training
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# agent = TestAgent(policy_net, value_net, memory, config, target_value_net = target_net, logger = logger)
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agent = rltorch.agents.QEPAgent(policy_net, value_net, memory, config, target_value_net = target_net, logger = logger)
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print("Training...")
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train(runner, agent, config, logger = logger, logwriter = logwriter)
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# For profiling...
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@ -1,5 +1,4 @@
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import gym
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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@ -9,69 +8,57 @@ import rltorch.memory as M
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import rltorch.env as E
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from rltorch.action_selector import StochasticSelector
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from tensorboardX import SummaryWriter
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import torch.multiprocessing as mp
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import signal
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from copy import deepcopy
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class Value(nn.Module):
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#
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## Networks
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#
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class Policy(nn.Module):
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def __init__(self, state_size, action_size):
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super(Value, self).__init__()
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super(Policy, self).__init__()
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self.state_size = state_size
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self.action_size = action_size
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self.fc1 = rn.NoisyLinear(state_size, 64)
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self.fc_norm = nn.LayerNorm(64)
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self.value_fc = rn.NoisyLinear(64, 64)
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self.value_fc_norm = nn.LayerNorm(64)
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self.value = rn.NoisyLinear(64, 1)
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self.fc2 = rn.NoisyLinear(64, 64)
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self.fc2_norm = nn.LayerNorm(64)
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self.advantage_fc = rn.NoisyLinear(64, 64)
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self.advantage_fc_norm = nn.LayerNorm(64)
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self.advantage = rn.NoisyLinear(64, action_size)
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self.fc3 = rn.NoisyLinear(64, action_size)
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def forward(self, x):
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x = F.relu(self.fc_norm(self.fc1(x)))
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state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
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state_value = self.value(state_value)
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advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
|
||||
advantage = self.advantage(advantage)
|
||||
|
||||
x = F.softmax(state_value + advantage - advantage.mean(), dim = 1)
|
||||
|
||||
x = F.relu(self.fc2_norm(self.fc2(x)))
|
||||
x = F.softmax(self.fc3(x), dim = 1)
|
||||
return x
|
||||
|
||||
|
||||
#
|
||||
## Configuration
|
||||
#
|
||||
config = {}
|
||||
config['seed'] = 901
|
||||
config['environment_name'] = 'Acrobot-v1'
|
||||
config['memory_size'] = 2000
|
||||
config['total_training_episodes'] = 500
|
||||
config['total_evaluation_episodes'] = 10
|
||||
config['batch_size'] = 32
|
||||
config['learning_rate'] = 1e-3
|
||||
config['target_sync_tau'] = 1e-1
|
||||
config['discount_rate'] = 0.99
|
||||
config['replay_skip'] = 0
|
||||
# How many episodes between printing out the episode stats
|
||||
config['print_stat_n_eps'] = 1
|
||||
config['disable_cuda'] = False
|
||||
|
||||
def train(env, agent, actor, memory, config, logger = None, logwriter = None):
|
||||
finished = False
|
||||
episode_num = 1
|
||||
while not finished:
|
||||
rltorch.env.simulateEnvEps(env, actor, config, memory = memory, logger = logger, name = "Training")
|
||||
episode_num += 1
|
||||
agent.learn()
|
||||
# When the episode number changes, log network paramters
|
||||
if logwriter is not None:
|
||||
agent.net.log_named_parameters()
|
||||
logwriter.write(logger)
|
||||
finished = episode_num > config['total_training_episodes']
|
||||
|
||||
#
|
||||
## Training Loop
|
||||
#
|
||||
def train(runner, agent, config, logger = None, logwriter = None):
|
||||
finished = False
|
||||
while not finished:
|
||||
runner.run()
|
||||
agent.learn()
|
||||
# When the episode number changes, log network paramters
|
||||
if logwriter is not None:
|
||||
agent.net.log_named_parameters()
|
||||
logwriter.write(logger)
|
||||
finished = runner.episode_num > config['total_training_episodes']
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
@ -93,11 +80,9 @@ if __name__ == "__main__":
|
|||
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
net = rn.Network(Value(state_size, action_size),
|
||||
net = rn.Network(Policy(state_size, action_size),
|
||||
torch.optim.Adam, config, device = device, name = "DQN")
|
||||
target_net = rn.TargetNetwork(net, device = device)
|
||||
net.model.share_memory()
|
||||
target_net.model.share_memory()
|
||||
|
||||
# Memory stores experiences for later training
|
||||
memory = M.EpisodeMemory()
|
||||
|
|
@ -108,9 +93,11 @@ if __name__ == "__main__":
|
|||
# Agent is what performs the training
|
||||
agent = rltorch.agents.REINFORCEAgent(net, memory, config, target_net = target_net, logger = logger)
|
||||
|
||||
print("Training...")
|
||||
# Runner performs one episode in the environment
|
||||
runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
|
||||
|
||||
train(env, agent, actor, memory, config, logger = logger, logwriter = logwriter)
|
||||
print("Training...")
|
||||
train(runner, agent, config, logger = logger, logwriter = logwriter)
|
||||
|
||||
# For profiling...
|
||||
# import cProfile
|
||||
|
|
|
|||
|
|
@ -1,135 +0,0 @@
|
|||
import gym
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import rltorch
|
||||
import rltorch.network as rn
|
||||
import rltorch.memory as M
|
||||
import rltorch.env as E
|
||||
from rltorch.action_selector import ArgMaxSelector
|
||||
from tensorboardX import SummaryWriter
|
||||
import torch.multiprocessing as mp
|
||||
|
||||
class Value(nn.Module):
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Value, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
|
||||
self.fc1 = rn.NoisyLinear(state_size, 255)
|
||||
self.fc_norm = nn.LayerNorm(255)
|
||||
|
||||
self.value_fc = rn.NoisyLinear(255, 255)
|
||||
self.value_fc_norm = nn.LayerNorm(255)
|
||||
self.value = rn.NoisyLinear(255, 1)
|
||||
|
||||
self.advantage_fc = rn.NoisyLinear(255, 255)
|
||||
self.advantage_fc_norm = nn.LayerNorm(255)
|
||||
self.advantage = rn.NoisyLinear(255, action_size)
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
|
||||
state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
|
||||
state_value = self.value(state_value)
|
||||
|
||||
advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
|
||||
advantage = self.advantage(advantage)
|
||||
|
||||
x = state_value + advantage - advantage.mean()
|
||||
|
||||
return x
|
||||
|
||||
|
||||
config = {}
|
||||
config['seed'] = 901
|
||||
config['environment_name'] = 'Acrobot-v1'
|
||||
config['memory_size'] = 2000
|
||||
config['total_training_episodes'] = 50
|
||||
config['total_evaluation_episodes'] = 5
|
||||
config['batch_size'] = 32
|
||||
config['learning_rate'] = 1e-3
|
||||
config['target_sync_tau'] = 1e-1
|
||||
config['discount_rate'] = 0.99
|
||||
config['replay_skip'] = 0
|
||||
# How many episodes between printing out the episode stats
|
||||
config['print_stat_n_eps'] = 1
|
||||
config['disable_cuda'] = False
|
||||
# Prioritized vs Random Sampling
|
||||
# 0 - Random sampling
|
||||
# 1 - Only the highest prioirities
|
||||
config['prioritized_replay_sampling_priority'] = 0.6
|
||||
# How important are the weights for the loss?
|
||||
# 0 - Treat all losses equally
|
||||
# 1 - Lower the importance of high losses
|
||||
# Should ideally start from 0 and move your way to 1 to prevent overfitting
|
||||
config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)
|
||||
|
||||
def train(runner, agent, config, logger = None, logwriter = None):
|
||||
finished = False
|
||||
last_episode_num = 1
|
||||
while not finished:
|
||||
runner.run(config['replay_skip'] + 1)
|
||||
agent.learn()
|
||||
if logwriter is not None:
|
||||
if last_episode_num < runner.episode_num:
|
||||
last_episode_num = runner.episode_num
|
||||
agent.net.log_named_parameters()
|
||||
logwriter.write(logger)
|
||||
finished = runner.episode_num > config['total_training_episodes']
|
||||
|
||||
if __name__ == "__main__":
|
||||
torch.multiprocessing.set_sharing_strategy('file_system') # To not hit file descriptor memory limit
|
||||
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end = " ")
|
||||
env = E.TorchWrap(gym.make(config['environment_name']))
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
|
||||
# Logging
|
||||
logger = rltorch.log.Logger()
|
||||
# logwriter = rltorch.log.LogWriter(logger, SummaryWriter())
|
||||
logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
net = rn.Network(Value(state_size, action_size),
|
||||
torch.optim.Adam, config, device = device, name = "DQN", logger = logger)
|
||||
target_net = rn.TargetNetwork(net, device = device)
|
||||
net.model.share_memory()
|
||||
target_net.model.share_memory()
|
||||
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = ArgMaxSelector(net, action_size, device = device)
|
||||
# Memory stores experiences for later training
|
||||
memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
|
||||
# memory = M.ReplayMemory(capacity = config['memory_size'])
|
||||
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.env.EnvironmentRunSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
|
||||
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.DQNAgent(net, memory, config, target_net = target_net, logger = logger)
|
||||
|
||||
print("Training...")
|
||||
|
||||
train(runner, agent, config, logger = logger, logwriter = logwriter)
|
||||
|
||||
# For profiling...
|
||||
# import cProfile
|
||||
# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
|
||||
# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
|
||||
|
||||
print("Training Finished.")
|
||||
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
|
||||
print("Evaulations Done.")
|
||||
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
147
examples/pong.py
147
examples/pong.py
|
|
@ -1,147 +0,0 @@
|
|||
import gym
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import rltorch
|
||||
import rltorch.network as rn
|
||||
import rltorch.memory as M
|
||||
import rltorch.env as E
|
||||
from rltorch.action_selector import ArgMaxSelector
|
||||
from tensorboardX import SummaryWriter
|
||||
import torch.multiprocessing as mp
|
||||
|
||||
class Value(nn.Module):
|
||||
def __init__(self, state_size, action_size):
|
||||
super(Value, self).__init__()
|
||||
self.state_size = state_size
|
||||
self.action_size = action_size
|
||||
|
||||
self.conv1 = nn.Conv2d(4, 32, kernel_size = (8, 8), stride = (4, 4))
|
||||
self.conv_norm1 = nn.LayerNorm([32, 19, 19])
|
||||
self.conv2 = nn.Conv2d(32, 64, kernel_size = (4, 4), stride = (2, 2))
|
||||
self.conv_norm2 = nn.LayerNorm([64, 8, 8])
|
||||
self.conv3 = nn.Conv2d(64, 64, kernel_size = (3, 3), stride = (1, 1))
|
||||
self.conv_norm3 = nn.LayerNorm([64, 6, 6])
|
||||
|
||||
self.fc1 = rn.NoisyLinear(64 * 6 * 6, 384)
|
||||
self.fc_norm = nn.LayerNorm(384)
|
||||
|
||||
self.value_fc = rn.NoisyLinear(384, 384)
|
||||
self.value_fc_norm = nn.LayerNorm(384)
|
||||
self.value = rn.NoisyLinear(384, 1)
|
||||
|
||||
self.advantage_fc = rn.NoisyLinear(384, 384)
|
||||
self.advantage_fc_norm = nn.LayerNorm(384)
|
||||
self.advantage = rn.NoisyLinear(384, action_size)
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
x = F.relu(self.conv_norm1(self.conv1(x)))
|
||||
x = F.relu(self.conv_norm2(self.conv2(x)))
|
||||
x = F.relu(self.conv_norm3(self.conv3(x)))
|
||||
|
||||
# Makes batch_size dimension again
|
||||
x = x.view(-1, 64 * 6 * 6)
|
||||
x = F.relu(self.fc_norm(self.fc1(x)))
|
||||
|
||||
state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
|
||||
state_value = self.value(state_value)
|
||||
|
||||
advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
|
||||
advantage = self.advantage(advantage)
|
||||
|
||||
x = state_value + advantage - advantage.mean()
|
||||
|
||||
# For debugging purposes...
|
||||
if torch.isnan(x).any().item():
|
||||
print("WARNING NAN IN MODEL DETECTED")
|
||||
|
||||
return x
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
config = {}
|
||||
config['seed'] = 901
|
||||
config['environment_name'] = 'PongNoFrameskip-v4'
|
||||
config['memory_size'] = 5000
|
||||
config['total_training_episodes'] = 500
|
||||
config['total_evaluation_episodes'] = 10
|
||||
config['learning_rate'] = 1e-4
|
||||
config['target_sync_tau'] = 1e-3
|
||||
config['discount_rate'] = 0.99
|
||||
config['exploration_rate'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.1, end_value = 0.01, iterations = 5000)
|
||||
config['replay_skip'] = 4
|
||||
config['batch_size'] = 32 * (config['replay_skip'] + 1)
|
||||
# How many episodes between printing out the episode stats
|
||||
config['print_stat_n_eps'] = 1
|
||||
config['disable_cuda'] = False
|
||||
# Prioritized vs Random Sampling
|
||||
# 0 - Random sampling
|
||||
# 1 - Only the highest prioirities
|
||||
config['prioritized_replay_sampling_priority'] = 0.6
|
||||
# How important are the weights for the loss?
|
||||
# 0 - Treat all losses equally
|
||||
# 1 - Lower the importance of high losses
|
||||
# Should ideally start from 0 and move your way to 1 to prevent overfitting
|
||||
config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)
|
||||
|
||||
if __name__ == "__main__":
|
||||
torch.multiprocessing.set_sharing_strategy('file_system') # To not hit file descriptor memory limit
|
||||
|
||||
# Setting up the environment
|
||||
rltorch.set_seed(config['seed'])
|
||||
print("Setting up environment...", end = " ")
|
||||
env = E.FrameStack(E.TorchWrap(
|
||||
E.ProcessFrame(E.FireResetEnv(gym.make(config['environment_name'])),
|
||||
resize_shape = (80, 80), crop_bounds = [34, 194, 15, 145], grayscale = True))
|
||||
, 4)
|
||||
env.seed(config['seed'])
|
||||
print("Done.")
|
||||
|
||||
state_size = env.observation_space.shape[0]
|
||||
action_size = env.action_space.n
|
||||
|
||||
# Logging
|
||||
logger = rltorch.log.Logger()
|
||||
logwriter = rltorch.log.LogWriter(SummaryWriter())
|
||||
|
||||
# Setting up the networks
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
|
||||
net = rn.Network(Value(state_size, action_size),
|
||||
torch.optim.Adam, config, device = device, name = "DQN")
|
||||
target_net = rn.TargetNetwork(net, device = device)
|
||||
net.model.share_memory()
|
||||
target_net.model.share_memory()
|
||||
|
||||
# Actor takes a net and uses it to produce actions from given states
|
||||
actor = ArgMaxSelector(net, action_size, device = device)
|
||||
# Memory stores experiences for later training
|
||||
memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
|
||||
# memory = M.ReplayMemory(capacity = config['memory_size'])
|
||||
|
||||
# Runner performs a certain number of steps in the environment
|
||||
runner = rltorch.mp.EnvironmentRun(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
|
||||
|
||||
# Agent is what performs the training
|
||||
agent = rltorch.agents.DQNAgent(net, memory, config, target_net = target_net, logger = logger)
|
||||
|
||||
print("Training...")
|
||||
|
||||
train(runner, agent, config, logger = logger, logwriter = logwriter)
|
||||
|
||||
# For profiling...
|
||||
# import cProfile
|
||||
# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
|
||||
# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
|
||||
|
||||
print("Training Finished.")
|
||||
runner.terminate() # We don't need the extra process anymore
|
||||
|
||||
print("Evaluating...")
|
||||
rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
|
||||
print("Evaulations Done.")
|
||||
|
||||
logwriter.close() # We don't need to write anything out to disk anymore
|
||||
|
|
@ -1,12 +1,8 @@
|
|||
from copy import deepcopy
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch.distributions import Categorical
|
||||
import rltorch
|
||||
import rltorch.memory as M
|
||||
import collections
|
||||
import random
|
||||
|
||||
class A2CSingleAgent:
|
||||
def __init__(self, policy_net, value_net, memory, config, logger = None):
|
||||
|
|
@ -25,11 +21,11 @@ class A2CSingleAgent:
|
|||
|
||||
return discounted_rewards
|
||||
|
||||
|
||||
def learn(self):
|
||||
episode_batch = self.memory.recall()
|
||||
state_batch, action_batch, reward_batch, next_state_batch, done_batch, log_prob_batch = zip(*episode_batch)
|
||||
|
||||
# Send batches to the appropriate device
|
||||
state_batch = torch.cat(state_batch).to(self.value_net.device)
|
||||
reward_batch = torch.tensor(reward_batch).to(self.value_net.device)
|
||||
not_done_batch = ~torch.tensor(done_batch).to(self.value_net.device)
|
||||
|
|
@ -37,12 +33,16 @@ class A2CSingleAgent:
|
|||
log_prob_batch = torch.cat(log_prob_batch).to(self.value_net.device)
|
||||
|
||||
## Value Loss
|
||||
# In A2C, the value loss is the difference between the discounted reward and the value from the first state
|
||||
# The value of the first state is supposed to tell us the expected reward from the current policy of the whole episode
|
||||
value_loss = F.mse_loss(self._discount_rewards(reward_batch).sum(), self.value_net(state_batch[0]))
|
||||
self.value_net.zero_grad()
|
||||
value_loss.backward()
|
||||
self.value_net.step()
|
||||
|
||||
## Policy Loss
|
||||
# Increase probabilities of advantageous states
|
||||
# and decrease the probabilities of non-advantageous ones
|
||||
with torch.no_grad():
|
||||
state_values = self.value_net(state_batch)
|
||||
next_state_values = torch.zeros_like(state_values)
|
||||
|
|
@ -61,8 +61,7 @@ class A2CSingleAgent:
|
|||
policy_loss.backward()
|
||||
self.policy_net.step()
|
||||
|
||||
|
||||
# Memory is irrelevant for future training
|
||||
# Memory under the old policy is not needed for future training
|
||||
self.memory.clear()
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -55,6 +55,7 @@ class DQNAgent:
|
|||
|
||||
expected_values = (reward_batch + (self.config['discount_rate'] * best_next_state_value)).unsqueeze(1)
|
||||
|
||||
# If we're sampling by TD error, multiply loss by a importance weight which helps decrease overfitting
|
||||
if (isinstance(self.memory, M.PrioritizedReplayMemory)):
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loss = (torch.as_tensor(importance_weights, device = self.net.device) * ((obtained_values - expected_values)**2).squeeze(1)).mean()
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else:
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@ -74,6 +75,7 @@ class DQNAgent:
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else:
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self.target_net.sync()
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# If we're sampling by TD error, readjust the weights of the experiences
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if (isinstance(self.memory, M.PrioritizedReplayMemory)):
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td_error = (obtained_values - expected_values).detach().abs()
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self.memory.update_priorities(batch_indexes, td_error)
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|
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@ -31,6 +31,7 @@ class PPOAgent:
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episode_batch = self.memory.recall()
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state_batch, action_batch, reward_batch, next_state_batch, done_batch, log_prob_batch = zip(*episode_batch)
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||||
# Send batches to the appropriate device
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||||
state_batch = torch.cat(state_batch).to(self.value_net.device)
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||||
action_batch = torch.tensor(action_batch).to(self.value_net.device)
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reward_batch = torch.tensor(reward_batch).to(self.value_net.device)
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|
@ -39,12 +40,16 @@ class PPOAgent:
|
|||
log_prob_batch = torch.cat(log_prob_batch).to(self.value_net.device)
|
||||
|
||||
## Value Loss
|
||||
# In PPO, the value loss is the difference between the discounted reward and the value from the first state
|
||||
# The value of the first state is supposed to tell us the expected reward from the current policy of the whole episode
|
||||
value_loss = F.mse_loss(self._discount_rewards(reward_batch).sum(), self.value_net(state_batch[0]))
|
||||
self.value_net.zero_grad()
|
||||
value_loss.backward()
|
||||
self.value_net.step()
|
||||
|
||||
## Policy Loss
|
||||
# Increase probabilities of advantageous states
|
||||
# and decrease the probabilities of non-advantageous ones
|
||||
with torch.no_grad():
|
||||
state_values = self.value_net(state_batch)
|
||||
next_state_values = torch.zeros_like(state_values)
|
||||
|
|
@ -56,6 +61,7 @@ class PPOAgent:
|
|||
distributions = list(map(Categorical, action_probabilities))
|
||||
old_log_probs = torch.stack(list(map(lambda distribution, action: distribution.log_prob(action), distributions, action_batch)))
|
||||
|
||||
# For PPO we want to stay within a certain ratio of the old policy
|
||||
policy_ratio = torch.exp(log_prob_batch - old_log_probs) # Equivalent to (log_prob / old_log_prob)
|
||||
policy_loss1 = policy_ratio * advantages
|
||||
policy_loss2 = policy_ratio.clamp(min = 0.8, max = 1.2) * advantages # From original paper
|
||||
|
|
@ -72,7 +78,7 @@ class PPOAgent:
|
|||
self.policy_net.step()
|
||||
|
||||
|
||||
# Memory is irrelevant for future training
|
||||
# Memory under the old policy is not needed for future training
|
||||
self.memory.clear()
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -3,7 +3,8 @@ import torch
|
|||
from .Network import Network
|
||||
from copy import deepcopy
|
||||
|
||||
# [TODO] See if you need to move network to device
|
||||
# [TODO] Should we torch.no_grad the __call__?
|
||||
# What if we want to sometimes do gradient descent as well?
|
||||
class ESNetwork(Network):
|
||||
"""
|
||||
Network that functions from the paper Evolutionary Strategies (https://arxiv.org/abs/1703.03864)
|
||||
|
|
|
|||
Loading…
Reference in a new issue