def train(agent: DQNAgent, env: Env, episodes: int = 10_000):
display = False
progression = tqdm.trange(episodes,
desc=f"Training {agent.name}",
unit="episode")
fps = 0
for episode in progression:
state = env.reset()
mean_reward = 0
return_ = 0
x_pos = 0
for step in count(1):
t = time()
action = agent.act(np.asarray(state), explore=True)
next_state, reward, done, info = env.step(action)
agent.memorize(
Experience((state, next_state, action, done, reward)))
state = next_state
agent.learn()
mean_reward += (reward - mean_reward) / step
return_ += reward
x_pos = max(x_pos, info["x_pos"])
fps = fps * 0.9 + 0.1 / (time() - t)
if not step % 100:
try:
display = (yaml.load(
(PROJECT_DIRECTORY / "display.yml").read_text()).get(
agent.name, {}).get("display", False))
except:
pass
if display:
env.render()
if done or info["flag_get"]:
break
progression.set_description(
f"Training {agent.name}; "
f"Frames: {agent.step} ({fps:.0f} FPS); "
f"last progression: {x_pos} ({x_pos/3260:.1%}); "
f"eps: {agent.eps:.2f}")
agent.register_episode(
EpisodeMetrics(episode=episode,
x_pos=x_pos,
return_=return_,
steps=step))
agent.save_model()
if d:
t = 1
else:
t = 0
agent.memory.store_effect(last_stored_frame_idx, a, r, t)
inner_success.append(picked_up)
if d:
done = True
# 42 steps per episode it seems
if env_steps % 5 == 0:
for _ in range(20):
agent.learn()
n_param_steps += 1
# if n_param_steps % 100 == 0:
# agent.update_target_net()
s = s_
if np.sum(inner_success) > 0:
successful_eps.append(1)
else:
successful_eps.append(0)
if i % 10 == 0:
agent.update_target_net()