126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
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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import rltorch
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import rltorch.network as rn
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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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def __init__(self, state_size, action_size):
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super(Value, 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.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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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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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'] = 100
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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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def train(env, agent, actor, memory, 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, memory = memory, logger = logger, name = "Training")
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episode_num += 1
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agent.learn()
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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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agent.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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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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# 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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env = E.TorchWrap(gym.make(config['environment_name']))
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env.seed(config['seed'])
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print("Done.")
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state_size = env.observation_space.shape[0]
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action_size = env.action_space.n
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# Logging
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logger = rltorch.log.Logger()
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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.Network(Value(state_size, action_size),
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torch.optim.Adam, config, device = device, name = "DQN")
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target_net = rn.TargetNetwork(net, device = device)
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net.model.share_memory()
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target_net.model.share_memory()
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# Memory stores experiences for later training
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memory = M.EpisodeMemory()
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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, 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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print("Training...")
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train(env, agent, actor, memory, config, logger = logger, logwriter = logwriter)
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# For profiling...
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# import cProfile
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# cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
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# python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
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print("Training Finished.")
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print("Evaluating...")
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rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
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print("Evaulations Done.")
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logwriter.close() # We don't need to write anything out to disk anymore
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