n_actions=n_actions,
memory_size=memory_size,
batch_size=batch_size,
gamma=gamma,
alpha=alpha,
lr=lr,
action_bounds=action_bounds,
reward_scale=reward_scale)
if TRAIN:
for episode in range(1, MAX_EPISODES + 1):
state = env.reset()
episode_reward = 0
done = 0
start_time = time.time()
while not done:
action = agent.choose_action(state)
next_state, reward, done, _ = env.step(action)
agent.store(state, reward, done, action, next_state)
value_loss, q_loss, policy_loss = agent.train()
if episode % 250 == 0:
agent.save_weights()
episode_reward += reward
state = next_state
log(episode, start_time, episode_reward, value_loss, q_loss,
policy_loss, len(agent.memory))
else:
player = Play(env, agent)
player.evaluate()
brain.schedule_lr()
brain.schedule_clip_range(iteration)
episode_reward = evaluate_policy(env_name, brain, state_shape)
if iteration == 1:
running_reward = episode_reward
else:
running_reward = 0.99 * running_reward + 0.01 * episode_reward
if iteration % log_period == 0:
print(f"Iter: {iteration}| "
f"Ep_reward: {episode_reward:.3f}| "
f"Running_reward: {running_reward:.3f}| "
f"Total_loss: {total_loss:.3f}| "
f"Explained variance:{ev:.3f}| "
f"Entropy: {entropy:.3f}| "
f"Iter_duration: {time.time() - start_time:.3f}| "
f"Lr: {brain.scheduler.get_last_lr()}| "
f"Clip_range:{brain.epsilon:.3f}")
brain.save_params(iteration, running_reward)
with SummaryWriter(env_name + "/logs") as writer:
writer.add_scalar("running reward", running_reward, iteration)
writer.add_scalar("episode reward", episode_reward, iteration)
writer.add_scalar("explained variance", ev, iteration)
writer.add_scalar("loss", total_loss, iteration)
writer.add_scalar("entropy", entropy, iteration)
else:
play = Play(env_name, brain)
play.evaluate()
