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disable empirical normalizer updates on resume training #30

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disable empirical normalizer updates on resume training #30

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f2b721a
disable empirical normalizer updates on resume training
tasdep Jun 21, 2024
40e669d
Revert prev and register count in the state_dict so it is saved and l…
tasdep Jun 21, 2024
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Revert prev and register count in the state_dict so it is saved and l… 
…oaded with the module
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@tasdep
tasdep committed Jun 21, 2024
commit 40e669de47a54067b2077481817f4b9296aa6b0e
2 changes: 1 addition & 1 deletion rsl_rl/modules/normalizer.py
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Expand Up @@ -26,7 +26,7 @@ def __init__(self, shape, eps=1e-2, until=None):
self.register_buffer("_mean", torch.zeros(shape).unsqueeze(0))
self.register_buffer("_var", torch.ones(shape).unsqueeze(0))
self.register_buffer("_std", torch.ones(shape).unsqueeze(0))
self.count = 0
self.register_buffer("count", torch.tensor(0, dtype=torch.long))

@property
def mean(self):
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8 changes: 2 additions & 6 deletions rsl_rl/runners/on_policy_runner.py
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Expand Up @@ -42,12 +42,8 @@ def __init__(self, env: VecEnv, train_cfg, log_dir=None, device="cpu"):
self.save_interval = self.cfg["save_interval"]
self.empirical_normalization = self.cfg["empirical_normalization"]
if self.empirical_normalization:
if train_cfg.get("resume") == True:
until = 0
else:
until = 1.0e8
self.obs_normalizer = EmpiricalNormalization(shape=[num_obs], until=until).to(self.device)
self.critic_obs_normalizer = EmpiricalNormalization(shape=[num_critic_obs], until=until).to(self.device)
self.obs_normalizer = EmpiricalNormalization(shape=[num_obs], until=1.0e8).to(self.device)
self.critic_obs_normalizer = EmpiricalNormalization(shape=[num_critic_obs], until=1.0e8).to(self.device)
else:
self.obs_normalizer = torch.nn.Identity() # no normalization
self.critic_obs_normalizer = torch.nn.Identity() # no normalization
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