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fix: Flashsac for g1_walk_flat #320

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Merged
TATP-233 merged 1 commit into from
Apr 20, 2026
Merged

fix: Flashsac for g1_walk_flat #320

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26 changes: 20 additions & 6 deletions conf/offpolicy/task/flashsac/g1_walk_flat/mujoco.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -5,17 +5,31 @@ training:
algo:
num_envs: 2048
learning_starts: 49
max_iterations: 5000
max_iterations: 6000
save_interval: 1000
updates_per_step: 8
#use_symmetry: true
replay_buffer_n: 1024
env:
control_config:
action_scale: 1.0
gait_phase_init_mode: "offset_phase"
reset_base_qvel_limit: 0.5
noise_config:
scale_gyro: 0.0
scale_gravity: 0.0
scale_joint_angle: 0.01
scale_joint_vel: 0.1
scale_linvel: 0.0
reward:
scales:
tracking_lin_vel: 2.0
tracking_ang_vel: 1.5
penalty_ang_vel_xy: -1.0
penalty_ang_vel_xy: -1.5
penalty_orientation: -10.0
penalty_action_rate: -2.0
pose: -0.5
penalty_feet_ori: -25.0
penalty_action_rate: -4.0
pose: -0.8
penalty_feet_ori: -20.0
feet_phase: 5.0
alive: 10.0
tracking_sigma: 0.25
Expand All @@ -24,6 +38,6 @@ reward:
max_tilt_deg: 65.0
gait_frequency: 1.5
feet_phase_swing_height: 0.09
feet_phase_tracking_sigma: 0.008
feet_phase_tracking_sigma: 0.025
close_feet_threshold: 0.15
pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
29 changes: 19 additions & 10 deletions src/unilab/algos/torch/flash_sac/learner.py
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Expand Up @@ -298,28 +298,32 @@ def update_critic(self, batch: dict[str, torch.Tensor]) -> dict[str, float]:

gamma = self.gamma**self.n_step

obs_all = torch.cat([critic_obs, critic_next_obs], dim=0)

with torch.no_grad():
with self._autocast():
next_actions, actor_info = self.actor(next_obs, training=False)
entropy_bonus = -self.temperature().detach() * actor_info["log_prob"]
next_q_values, next_q_info = self.target_critic(
critic_next_obs, next_actions, training=False
)
next_q_log_probs = select_min_q_log_probs(next_q_values, next_q_info["log_prob"])
actor_entropy = self.temperature().detach() * actor_info["log_prob"]
act_all = torch.cat([actions, next_actions], dim=0)
qs_all, q_info_all = self.target_critic(obs_all, act_all, training=True)
next_q_values = qs_all.chunk(2, dim=1)[1]
next_q_log_probs_full = q_info_all["log_prob"].chunk(2, dim=1)[1]
next_q_log_probs = select_min_q_log_probs(next_q_values, next_q_log_probs_full)
support = cast(torch.Tensor, self.target_critic.predictor.support)
target_probs = compute_categorical_td_target(
support=support,
target_log_probs=next_q_log_probs,
reward=rewards,
terminated=terminated,
truncated=truncated,
actor_entropy=entropy_bonus,
actor_entropy=actor_entropy,
gamma=gamma,
)

with self._autocast():
_, pred_info = self.critic(critic_obs, actions, training=True)
critic_loss = -(target_probs.unsqueeze(0) * pred_info["log_prob"]).sum(dim=-1).mean()
_, pred_info_all = self.critic(obs_all, act_all, training=True)
pred_log_probs = pred_info_all["log_prob"].chunk(2, dim=1)[0]
critic_loss = -(target_probs.unsqueeze(0) * pred_log_probs).sum(dim=-1).mean()

self.critic_optimizer.zero_grad(set_to_none=True)
if self.scaler is not None:
Expand All @@ -343,16 +347,21 @@ def update_critic(self, batch: dict[str, torch.Tensor]) -> dict[str, float]:

def update_actor(self, batch: dict[str, torch.Tensor]) -> dict[str, float]:
obs = batch["obs"].to(self.device)
next_obs = batch["next_obs"].to(self.device)
expert_actions = batch["actions"].to(self.device)
critic_obs = batch.get("critic")
critic_obs = critic_obs.to(self.device) if critic_obs is not None else None

obs = self._maybe_normalize_obs(obs, update=False)
next_obs = self._maybe_normalize_obs(next_obs, update=False)
critic_obs = critic_obs if critic_obs is not None else obs

obs_all = torch.cat([obs, next_obs], dim=0)

with self._autocast():
actions, actor_info = self.actor(obs, training=True)
log_probs = actor_info["log_prob"]
actions_all, actor_info_all = self.actor(obs_all, training=True)
actions = actions_all.chunk(2, dim=0)[0]
log_probs = actor_info_all["log_prob"].chunk(2, dim=0)[0]

self._set_requires_grad(self.critic, False)
q_values, _ = self.critic(critic_obs, actions, training=False)
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