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Configuration error
Configuration error
| Setting up Training Environment... | |
| Creating Liquid PPO Agent... | |
| Using cpu device | |
| Wrapping the env with a `Monitor` wrapper | |
| Wrapping the env in a DummyVecEnv. | |
| Starting Training (This may take a while)... | |
| ---------------------------------- | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -1.79e+04 | | |
| | time/ | | | |
| | fps | 465 | | |
| | iterations | 1 | | |
| | time_elapsed | 4 | | |
| | total_timesteps | 2048 | | |
| ---------------------------------- | |
| ----------------------------------------- | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -1.91e+04 | | |
| | time/ | | | |
| | fps | 24 | | |
| | iterations | 2 | | |
| | time_elapsed | 169 | | |
| | total_timesteps | 4096 | | |
| | train/ | | | |
| | approx_kl | 0.004881427 | | |
| | clip_fraction | 0.0184 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.67 | | |
| | explained_variance | 0.000221 | | |
| | learning_rate | 0.0003 | | |
| | loss | 4.46e+04 | | |
| | n_updates | 10 | | |
| | policy_gradient_loss | -0.00155 | | |
| | std | 0.998 | | |
| | value_loss | 9.86e+04 | | |
| ----------------------------------------- | |
| ------------------------------------------ | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.01e+04 | | |
| | time/ | | | |
| | fps | 31 | | |
| | iterations | 3 | | |
| | time_elapsed | 196 | | |
| | total_timesteps | 6144 | | |
| | train/ | | | |
| | approx_kl | 0.0020806824 | | |
| | clip_fraction | 0.00215 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.66 | | |
| | explained_variance | -6.6e-05 | | |
| | learning_rate | 0.0003 | | |
| | loss | 6.96e+04 | | |
| | n_updates | 20 | | |
| | policy_gradient_loss | -0.000315 | | |
| | std | 0.996 | | |
| | value_loss | 1.37e+05 | | |
| ------------------------------------------ | |
| ------------------------------------------ | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.08e+04 | | |
| | time/ | | | |
| | fps | 36 | | |
| | iterations | 4 | | |
| | time_elapsed | 226 | | |
| | total_timesteps | 8192 | | |
| | train/ | | | |
| | approx_kl | 0.0041169263 | | |
| | clip_fraction | 0.0206 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.66 | | |
| | explained_variance | -5.96e-06 | | |
| | learning_rate | 0.0003 | | |
| | loss | 7.12e+04 | | |
| | n_updates | 30 | | |
| | policy_gradient_loss | -0.00234 | | |
| | std | 0.994 | | |
| | value_loss | 1.63e+05 | | |
| ------------------------------------------ | |
| ----------------------------------------- | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.1e+04 | | |
| | time/ | | | |
| | fps | 42 | | |
| | iterations | 5 | | |
| | time_elapsed | 242 | | |
| | total_timesteps | 10240 | | |
| | train/ | | | |
| | approx_kl | 0.003560378 | | |
| | clip_fraction | 0.0138 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.66 | | |
| | explained_variance | 6.62e-06 | | |
| | learning_rate | 0.0003 | | |
| | loss | 8.45e+04 | | |
| | n_updates | 40 | | |
| | policy_gradient_loss | -0.00122 | | |
| | std | 0.998 | | |
| | value_loss | 1.94e+05 | | |
| ----------------------------------------- | |
| ------------------------------------------ | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.13e+04 | | |
| | time/ | | | |
| | fps | 47 | | |
| | iterations | 6 | | |
| | time_elapsed | 258 | | |
| | total_timesteps | 12288 | | |
| | train/ | | | |
| | approx_kl | 0.0027945049 | | |
| | clip_fraction | 0.0139 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.67 | | |
| | explained_variance | 2.8e-06 | | |
| | learning_rate | 0.0003 | | |
| | loss | 6.45e+04 | | |
| | n_updates | 50 | | |
| | policy_gradient_loss | -0.00151 | | |
| | std | 1 | | |
| | value_loss | 1.47e+05 | | |
| ------------------------------------------ | |
| ----------------------------------------- | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.19e+04 | | |
| | time/ | | | |
| | fps | 52 | | |
| | iterations | 7 | | |
| | time_elapsed | 274 | | |
| | total_timesteps | 14336 | | |
| | train/ | | | |
| | approx_kl | 0.004093081 | | |
| | clip_fraction | 0.018 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.68 | | |
| | explained_variance | 1.79e-07 | | |
| | learning_rate | 0.0003 | | |
| | loss | 9.83e+04 | | |
| | n_updates | 60 | | |
| | policy_gradient_loss | -0.00117 | | |
| | std | 1 | | |
| | value_loss | 1.69e+05 | | |
| ----------------------------------------- | |
| ------------------------------------------ | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.21e+04 | | |
| | time/ | | | |
| | fps | 56 | | |
| | iterations | 8 | | |
| | time_elapsed | 290 | | |
| | total_timesteps | 16384 | | |
| | train/ | | | |
| | approx_kl | 0.0041737873 | | |
| | clip_fraction | 0.0396 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.67 | | |
| | explained_variance | -2.38e-07 | | |
| | learning_rate | 0.0003 | | |
| | loss | 9.31e+04 | | |
| | n_updates | 70 | | |
| | policy_gradient_loss | -0.00342 | | |
| | std | 0.996 | | |
| | value_loss | 1.76e+05 | | |
| ------------------------------------------ | |
| ------------------------------------------ | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.21e+04 | | |
| | time/ | | | |
| | fps | 59 | | |
| | iterations | 9 | | |
| | time_elapsed | 309 | | |
| | total_timesteps | 18432 | | |
| | train/ | | | |
| | approx_kl | 0.0060277004 | | |
| | clip_fraction | 0.037 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.65 | | |
| | explained_variance | 2.21e-06 | | |
| | learning_rate | 0.0003 | | |
| | loss | 6.73e+04 | | |
| | n_updates | 80 | | |
| | policy_gradient_loss | -0.00309 | | |
| | std | 0.991 | | |
| | value_loss | 1.55e+05 | | |
| ------------------------------------------ | |
| ------------------------------------------ | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.32e+04 | | |
| | time/ | | | |
| | fps | 62 | | |
| | iterations | 10 | | |
| | time_elapsed | 326 | | |
| | total_timesteps | 20480 | | |
| | train/ | | | |
| | approx_kl | 0.0032014307 | | |
| | clip_fraction | 0.0083 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.63 | | |
| | explained_variance | 2.98e-07 | | |
| | learning_rate | 0.0003 | | |
| | loss | 8.79e+04 | | |
| | n_updates | 90 | | |
| | policy_gradient_loss | -0.000593 | | |
| | std | 0.989 | | |
| | value_loss | 1.53e+05 | | |
| ------------------------------------------ | |
| ------------------------------------------ | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.57e+04 | | |
| | time/ | | | |
| | fps | 64 | | |
| | iterations | 11 | | |
| | time_elapsed | 348 | | |
| | total_timesteps | 22528 | | |
| | train/ | | | |
| | approx_kl | 0.0035356751 | | |
| | clip_fraction | 0.012 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.61 | | |
| | explained_variance | 5.96e-08 | | |
| | learning_rate | 0.0003 | | |
| | loss | 1.91e+05 | | |
| | n_updates | 100 | | |
| | policy_gradient_loss | -0.00137 | | |
| | std | 0.981 | | |
| | value_loss | 3.78e+05 | | |
| ------------------------------------------ | |
| ------------------------------------------ | |
| | rollout/ | | | |
| | ep_len_mean | 1e+03 | | |
| | ep_rew_mean | -2.78e+04 | | |
| | time/ | | | |
| | fps | 67 | | |
| | iterations | 12 | | |
| | time_elapsed | 365 | | |
| | total_timesteps | 24576 | | |
| | train/ | | | |
| | approx_kl | 0.0027306664 | | |
| | clip_fraction | 0.00293 | | |
| | clip_range | 0.2 | | |
| | entropy_loss | -5.6 | | |
| | explained_variance | 1.79e-07 | | |
| | learning_rate | 0.0003 | | |
| | loss | 4.31e+05 | | |
| | n_updates | 110 | | |
| | policy_gradient_loss | -0.000544 | | |
| | std | 0.983 | | |
| | value_loss | 9.15e+05 | | |
| ------------------------------------------ | |
| Traceback (most recent call last): | |
| File "/home/ylop/Documents/drone go brr/Drone-go-brrrrr/Drone-go-brrrrr/train.py", line 35, in <module> | |
| train() | |
| ~~~~~^^ | |
| File "/home/ylop/Documents/drone go brr/Drone-go-brrrrr/Drone-go-brrrrr/train.py", line 28, in train | |
| model.learn(total_timesteps=total_timesteps, callback=checkpoint_callback) | |
| ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | |
| File "/home/ylop/.local/lib/python3.14/site-packages/stable_baselines3/ppo/ppo.py", line 311, in learn | |
| return super().learn( | |
| ~~~~~~~~~~~~~^ | |
| total_timesteps=total_timesteps, | |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | |
| ...<4 lines>... | |
| progress_bar=progress_bar, | |
| ^^^^^^^^^^^^^^^^^^^^^^^^^^ | |
| ) | |
| ^ | |
| File "/home/ylop/.local/lib/python3.14/site-packages/stable_baselines3/common/on_policy_algorithm.py", line 324, in learn | |
| continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps) | |
| File "/home/ylop/.local/lib/python3.14/site-packages/stable_baselines3/common/on_policy_algorithm.py", line 202, in collect_rollouts | |
| actions, values, log_probs = self.policy(obs_tensor) | |
| ~~~~~~~~~~~^^^^^^^^^^^^ | |
| File "/home/ylop/.local/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl | |
| return self._call_impl(*args, **kwargs) | |
| ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ | |
| File "/home/ylop/.local/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1786, in _call_impl | |
| return forward_call(*args, **kwargs) | |
| File "/home/ylop/.local/lib/python3.14/site-packages/stable_baselines3/common/policies.py", line 645, in forward | |
| features = self.extract_features(obs) | |
| File "/home/ylop/.local/lib/python3.14/site-packages/stable_baselines3/common/policies.py", line 672, in extract_features | |
| return super().extract_features(obs, self.features_extractor if features_extractor is None else features_extractor) | |
| ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ | |
| File "/home/ylop/.local/lib/python3.14/site-packages/stable_baselines3/common/policies.py", line 131, in extract_features | |
| return features_extractor(preprocessed_obs) | |
| File "/home/ylop/.local/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl | |
| return self._call_impl(*args, **kwargs) | |
| ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ | |
| File "/home/ylop/.local/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1786, in _call_impl | |
| return forward_call(*args, **kwargs) | |
| File "/home/ylop/Documents/drone go brr/Drone-go-brrrrr/Drone-go-brrrrr/models/liquid_ppo.py", line 58, in forward | |
| output, self.hx = self.ltc(observations, self.hx) | |
| ~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^ | |
| File "/home/ylop/.local/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl | |
| return self._call_impl(*args, **kwargs) | |
| ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ | |
| File "/home/ylop/.local/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1786, in _call_impl | |
| return forward_call(*args, **kwargs) | |
| File "/home/ylop/.local/lib/python3.14/site-packages/ncps/torch/ltc.py", line 185, in forward | |
| h_out, h_state = self.rnn_cell.forward(inputs, h_state, ts) | |
| ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^ | |
| File "/home/ylop/.local/lib/python3.14/site-packages/ncps/torch/ltc_cell.py", line 282, in forward | |
| next_state = self._ode_solver(inputs, states, elapsed_time) | |
| File "/home/ylop/.local/lib/python3.14/site-packages/ncps/torch/ltc_cell.py", line 247, in _ode_solver | |
| v_pre = numerator / (denominator + self._epsilon) | |
| ^^^^^ | |
| KeyboardInterrupt | |