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Corrected A2C and PPO to train at the end of an episode

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This commit is contained in:
Brandon Rozek 2019-03-01 21:04:13 -05:00
parent 1958fc7c7e
commit e42f5bba1b
5 changed files with 48 additions and 28 deletions
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13
examples/acrobot_a2c.py
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@ -94,15 +94,12 @@ config['disable_cuda'] = False
def train(runner, agent, config, logger = None, logwriter = None):
finished = False
last_episode_num = 1
while not finished:
runner.run(config['replay_skip'] + 1)
runner.run()
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()
agent.value_net.log_named_parameters()
agent.policy_net.log_named_parameters()
logwriter.write(logger)
finished = runner.episode_num > config['total_training_episodes']
@ -141,8 +138,8 @@ if __name__ == "__main__":
# agent = rltorch.agents.REINFORCEAgent(net, memory, config, target_net = target_net, logger = logger)
agent = rltorch.agents.A2CSingleAgent(policy_net, value_net, memory, config, logger = logger)
# Runner performs a certain number of steps in the environment
runner = rltorch.env.EnvironmentRunSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
# Runner performs one episode in the environment
runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
print("Training...")
train(runner, agent, config, logger = logger, logwriter = logwriter)

13
examples/acrobot_ppo.py
View file

@ -94,21 +94,16 @@ config['disable_cuda'] = False
def train(runner, agent, config, logger = None, logwriter = None):
finished = False
last_episode_num = 1
while not finished:
runner.run(config['replay_skip'] + 1)
runner.run()
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()
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__":
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 = " ")
@ -142,7 +137,7 @@ if __name__ == "__main__":
agent = rltorch.agents.PPOAgent(policy_net, value_net, memory, config, logger = logger)
# Runner performs a certain number of steps in the environment
runner = rltorch.env.EnvironmentRunSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
runner = rltorch.env.EnvironmentEpisodeSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
print("Training...")
train(runner, agent, config, logger = logger, logwriter = logwriter)