class ActorCriticAgent:
""" Advantage Actor Critic agent """
def __init__(self, num_actions, checkpoint=None):
self.network, self.trainable_parameters = self.init_network(
num_actions)
self.optimizer = torch.optim.Adam(self.trainable_parameters, lr=1e-4)
self.memory = Memory()
if checkpoint is not None:
load_checkpoint(self.network, self.optimizer, checkpoint)
def init_network(self, num_actions):
network = {'actor_critic': ActorCritic(num_actions)}
trainable_parameters = list(network['actor_critic'].parameters())
return network, trainable_parameters
def play(self,
environment,
max_games=1,
max_steps=500,
train=False,
verbose=False,
recorder=None):
n_steps = 0
n_games = 0
current_game_infos = {
'game': n_games + 1,
'reward': 0,
'game_duration': 0
}
observation = environment.reset()
if recorder is not None:
recorder.reset()
recorder.record(environment)
while (n_steps < max_steps) and (n_games < max_games):
self.init_rollout(observation)
for rollout_step in range(20):
value, log_policy, action = self.network['actor_critic'](
observation)
self.memory.append({
'value': value,
'log_policy': log_policy,
'action': action
})
observation, extrinsic_reward, is_game_over, infos = environment.step(
action.numpy()[0])
if recorder is not None:
recorder.record(environment)
reward = self.get_reward(observation, extrinsic_reward)
self.memory.append({'reward': reward})
current_game_infos['reward'] += reward
current_game_infos['game_duration'] += 1
n_steps += 1
if is_game_over:
n_games += 1
print(current_game_infos)
current_game_infos = {
'game': n_games + 1,
'reward': 0,
'game_duration': 0
}
observation = environment.reset()
break
self.end_rollout(observation, is_game_over)
if verbose:
print(current_game_infos)
if train:
loss = self.compute_loss()
self.backpropagate(loss)
if recorder is not None:
recorder.stop()
def init_rollout(self, observation):
self.memory.reset()
self.network['actor_critic'].detach_internal_state()
def end_rollout(self, observation, is_game_over):
if is_game_over:
next_value = torch.Tensor([[0]])
self.network['actor_critic'].reset_internal_state()
else:
next_value = self.network['actor_critic'](observation)[0].detach()
self.memory.append({'value': next_value})
def get_reward(self, observation, extrinsic_reward):
return np.clip(extrinsic_reward, -1, 1)
def compute_loss(self):
loss = self.network['actor_critic'].loss(self.memory)
return loss
def backpropagate(self, loss, max_gradient_norm=40):
self.optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.trainable_parameters,
max_gradient_norm)
self.optimizer.step()
def run(self):
total_time = time.time()
steps = 0
try:
save_interval = 0
memory = Memory()
do_training = True
while do_training:
episode_time = time.time()
self.env.initial_position = {
'p_x': np.random.uniform(1, 4),
'p_y': 3.7,
'p_z': 0.05,
'o_x': 0,
'o_y': 0.0,
'o_z': np.random.uniform(0.4, 1),
'o_w': 0.855
}
state = self.env.reset()
state = preprocess(state, self.img_y_offset, self.img_x_scale,
self.img_y_scale)
# state = np.expand_dims(state, axis=0)
done = False
cumulated_reward = 0
stacked_states = deque(maxlen=self.n_frames)
stacked_next_states = deque(maxlen=self.n_frames)
for i in range(self.n_frames):
stacked_states.append(state)
stacked_next_states.append(state)
episode_steps = 0
while not done:
steps += 1
episode_steps += 1
action = self.agent.act(
np.expand_dims(np.stack(stacked_states, axis=2),
axis=0))
next_state, reward, done, _ = self.env.step(action)
next_state = preprocess(next_state, self.img_y_offset,
self.img_x_scale, self.img_y_scale)
stacked_next_states.append(next_state)
memory.append(action, np.stack(stacked_states, axis=2),
np.stack(stacked_next_states, axis=2),
reward, done)
stacked_states.append(next_state)
cumulated_reward += reward
save_interval += 1
if save_interval >= self.sample_batch_size:
save_interval = 0
replay_time = time.time()
self.agent.replay(memory)
rospy.loginfo("Replay time {}".format(time.time() -
replay_time))
if steps >= self.sample_batch_size * 200:
do_training = False
memory = Memory()
if self.highest_reward < cumulated_reward:
self.highest_reward = cumulated_reward
rospy.loginfo("total episode_steps {}, reward {}/{}".format(
episode_steps, cumulated_reward, self.highest_reward))
rospy.loginfo("Episode time {}, total {}".format(
self.format_time(episode_time),
self.format_time(total_time)))
rospy.loginfo("exploration_rate {}".format(
self.agent.exploration_rate))
finally:
self.env.close()
rospy.loginfo("Total time: {}".format(
self.format_time(total_time)))
rospy.loginfo("Total steps: {}".format(steps))
