def setUp(self) -> None:
self.temp_dir = tempfile.TemporaryDirectory()
episodes = 80
seeds = [0, 1, 3, 4, 5]
experiment_name = "test_env"
logger = Logger(
output_path=Path(self.temp_dir.name),
experiment_name=experiment_name,
step_write_frequency=None,
episode_write_frequency=None,
)
benchmark = SigmoidBenchmark()
env = benchmark.get_benchmark()
agent = RandomAgent(env)
logger.set_env(env)
env_logger = logger.add_module(env)
for seed in seeds:
env.seed(seed)
logger.set_additional_info(seed=seed)
logger.reset_episode()
for episode in range(episodes):
state = env.reset()
done = False
reward = 0
step = 0
while not done:
action = agent.act(state, reward)
env_logger.log(
"logged_step",
step,
)
env_logger.log(
"logged_episode",
episode,
)
next_state, reward, done, _ = env.step(action)
env_logger.log(
"reward",
reward,
)
env_logger.log(
"done",
done,
)
agent.train(next_state, reward)
state = next_state
logger.next_step()
step += 1
agent.end_episode(state, reward)
logger.next_episode()
env.close()
logger.close()
self.log_file = env_logger.log_file.name
def train_chainer(
agent, env, num_episodes=10, flatten_state=False, logger: Logger = None
):
for i in range(num_episodes):
state = env.reset()
if flatten_state:
state = np.array(flatten([state[k] for k in state.keys()]))
state = state.astype(np.float32)
done = False
r = 0
reward = 0
while not done:
action = agent.act_and_train(state, reward)
next_state, reward, done, _ = env.step(action)
r += reward
if flatten_state:
state = np.array(flatten([next_state[k] for k in next_state.keys()]))
state = state.astype(np.float32)
else:
state = next_state
if logger is not None:
logger.next_step()
agent.stop_episode_and_train(state, reward, done=done)
if logger is not None:
logger.next_episode()
print(
f"Episode {i}/{num_episodes}...........................................Reward: {r}"
)
# Make chainer agent
obs_size = env.observation_space.low.size
action_size = env.action_space.low.size
agent = make_chainer_a3c(obs_size, action_size)
# Training
num_episodes = 3
for i in range(num_episodes):
# Reset environment to begin episode
state = env.reset()
# Initialize episode
done = False
r = 0
reward = 0
while not done:
# Select action
action = agent.act_and_train(state, reward)
# Execute action
next_state, reward, done, _ = env.step(action)
r += reward
logger.next_step()
state = next_state
logger.next_episode()
# Train agent after episode has ended
agent.stop_episode_and_train(state, reward, done=done)
# Log episode
print(
f"Episode {i+1}/{num_episodes}...........................................Reward: {r}"
)
