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RL works at this commit - fixed actor.py and bugs in gym_manipulator

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Michel Aractingi
2025-08-03 23:21:13 +02:00
parent ff38a51df9
commit f49280e89b
2 changed files with 41 additions and 38 deletions
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30
src/lerobot/scripts/rl/actor.py
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@@ -243,7 +243,7 @@ def act_with_policy(
logging.info("make_env online")
online_env, teleop_device = make_robot_env(cfg=cfg.env)
env_processor, action_processor = make_processors(online_env, cfg.env, cfg.policy.device)
env_processor, action_processor = make_processors(online_env, cfg.env)
set_seed(cfg.seed)
device = get_safe_torch_device(cfg.policy.device, log=True)
@@ -288,18 +288,15 @@ def act_with_policy(
logging.info("[ACTOR] Shutting down act_with_policy")
return
if interaction_step >= cfg.policy.online_step_before_learning:
# Time policy inference and check if it meets FPS requirement
with policy_timer:
# Extract observation from transition for policy
batch_obs = transition[TransitionKey.OBSERVATION]
action = policy.select_action(batch=batch_obs)
policy_fps = policy_timer.fps_last
observation = transition[TransitionKey.OBSERVATION]
log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
# Time policy inference and check if it meets FPS requirement
with policy_timer:
# Extract observation from transition for policy
action = policy.select_action(batch=observation)
policy_fps = policy_timer.fps_last
else:
action = online_env.action_space.sample()
log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
# Use the new step function
new_transition, terminate_episode = step_env_and_process_transition(
@@ -312,10 +309,11 @@ def act_with_policy(
)
# Extract values from processed transition
next_observation = new_transition[TransitionKey.OBSERVATION]
executed_action = new_transition[TransitionKey.COMPLEMENTARY_DATA]["teleop_action"]
reward = new_transition[TransitionKey.REWARD]
done = new_transition.get(TransitionKey.DONE, False)
truncated = new_transition.get(TransitionKey.TRUNCATED, False)
processed_action = new_transition[TransitionKey.ACTION]
sum_reward_episode += float(reward)
episode_total_steps += 1
@@ -329,13 +327,13 @@ def act_with_policy(
# Create transition for learner (convert to old format)
list_transition_to_send_to_learner.append(
Transition(
state=transition[TransitionKey.OBSERVATION],
action=processed_action,
state=observation,
action=executed_action,
reward=reward,
next_state=new_transition[TransitionKey.OBSERVATION],
next_state=next_observation,
done=done,
truncated=truncated,
complementary_info=new_transition[TransitionKey.COMPLEMENTARY_DATA],
complementary_info={}, # new_transition[TransitionKey.COMPLEMENTARY_DATA],
)
)