agent.reset()
score = 0
actions = [np.array([0.0, 0.0, 0.0, 0.0])] * num_agents
# Train until environment ends the episode
while True:
for env_agent_idx in range(num_agents):
# Let deep learning agent act based on states
actions[env_agent_idx] = agent.act(states[env_agent_idx])
env_info = env.step(actions)[brain_name]
next_states = env_info.vector_observations
rewards = env_info.rewards
dones = env_info.local_done
for env_agent_idx in range(num_agents):
# Save to replay buffer
agent.memorize(states[env_agent_idx], actions[env_agent_idx], \
rewards[env_agent_idx], next_states[env_agent_idx], \
dones[env_agent_idx])
# Learn
agent.step()
states = next_states
score += np.sum(rewards) / len(rewards)
if np.any(dones):
break
# Check and track scores
scores_deque.append(score)
scores.append(score)
average_score = np.mean(scores_deque)
print('\rEpisode {}\tAverage Score: {:.2f}\tScore: {:.2f}'.format(
i_episode, average_score, score),
end="")
if i_episode % print_every == 0:
states = env_info.vector_observations
score = 0
steps = 0
# Train until environment ends the episode
while True:
steps += 1
# Let deep learning agent act based on states
action_0 = agent.act(states[0])
action_1 = agent.act(states[1])
# Send action to Unity environment
env_info = env.step([action_0, action_1])[brain_name]
states_next = env_info.vector_observations
rewards = env_info.rewards
dones = env_info.local_done
# Save experiences to replay buffer
agent.memorize(states[0], action_0, rewards[0], states_next[0],
dones[0])
agent.memorize(states[1], action_1, rewards[1], states_next[1],
dones[1])
# Learn
agent.update_step()
agent.update_step()
states = states_next
score += np.sum(rewards) / len(rewards)
if np.any(dones):
break
# Check and track scores
scores_deque.append(score)
scores.append(score)
score_average = np.mean(scores_deque)
print('\rEpisode {}\tAverage Score: {:.4f}\tScore: {:.4f}'.format(
i_episode, score_average, score),
