Implemented REINFORCE into the library

examples/acrobot_reinforce.py

@@ -0,0 +1,126 @@
+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 StochasticSelector
+from tensorboardX import SummaryWriter
+import torch.multiprocessing as mp
+import signal
+from copy import deepcopy
+
+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, 64)
+    self.fc_norm = nn.LayerNorm(64)
+    
+    self.value_fc = rn.NoisyLinear(64, 64)
+    self.value_fc_norm = nn.LayerNorm(64)
+    self.value = rn.NoisyLinear(64, 1)
+    
+    self.advantage_fc = rn.NoisyLinear(64, 64)
+    self.advantage_fc_norm = nn.LayerNorm(64)
+    self.advantage = rn.NoisyLinear(64, 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 = F.softmax(state_value + advantage - advantage.mean(), dim = 1)
+    
+    return x
+
+
+config = {}
+config['seed'] = 901
+config['environment_name'] = 'Acrobot-v1'
+config['memory_size'] = 2000
+config['total_training_episodes'] = 100
+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']
+
+
+
+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(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()
+
+  # Memory stores experiences for later training
+  memory = M.EpisodeMemory()
+
+  # Actor takes a net and uses it to produce actions from given states
+  actor = StochasticSelector(net, action_size, memory, device = device)
+
+  # Agent is what performs the training
+  agent = rltorch.agents.REINFORCEAgent(net, memory, config, target_net = target_net, logger = logger)
+    
+  print("Training...")
+
+  train(env, agent, actor, memory, 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