rltorch/examples/acrobot_qep.py

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
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, 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


class Policy(nn.Module):
  def __init__(self, state_size, action_size):
    super(Policy, self).__init__()
    self.state_size = state_size
    self.action_size = action_size

    self.fc1 = nn.Linear(state_size, 125)
    self.fc_norm = nn.LayerNorm(125)
    
    self.fc2 = nn.Linear(125, 125)
    self.fc2_norm = nn.LayerNorm(125)

    self.action_prob = nn.Linear(125, action_size)

  def forward(self, x):
    x = F.relu(self.fc_norm(self.fc1(x)))
    x = F.relu(self.fc2_norm(self.fc2(x)))
    x = F.softmax(self.action_prob(x), dim = 1)
    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.value_net.log_named_parameters()
            agent.policy_net.log_named_parameters()
          logwriter.write(logger)
        finished = runner.episode_num > config['total_training_episodes']

if __name__ == "__main__":
  # 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")
  config2 = deepcopy(config)
  config2['learning_rate'] = 0.01
  policy_net = rn.ESNetwork(Policy(state_size, action_size), 
                      torch.optim.Adam, 500, None, config2, sigma = 0.1, device = device, name = "ES", logger = logger)
  value_net = rn.Network(Value(state_size, action_size), 
                      torch.optim.Adam, config, device = device, name = "DQN", logger = logger)

  target_net = rn.TargetNetwork(value_net, device = device)
  value_net.model.share_memory()
  target_net.model.share_memory()

  # Actor takes a net and uses it to produce actions from given states
  actor = StochasticSelector(policy_net, action_size, device = device)
  # Memory stores experiences for later training
  memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])

  # 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 = TestAgent(policy_net, value_net, memory, config, target_value_net = target_net, logger = logger)
  agent = rltorch.agents.QEPAgent(policy_net, value_net, memory, config, target_value_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