Another commit, more steps

PandaReachDenseA2C-n9.zip

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PandaReachDenseA2C-n9/_stable_baselines3_version

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PandaReachDenseA2C-n9/data

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+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

PandaReachDenseA2C-n9/policy.optimizer.pth

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PandaReachDenseA2C-n9/policy.pth

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PandaReachDenseA2C-n9/pytorch_variables.pth

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PandaReachDenseA2C-n9/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.15.0-60-generic-x86_64-with-glibc2.35 # 66-Ubuntu SMP Fri Jan 20 14:29:49 UTC 2023
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -1.66 +/- 0.20
+      value: -1.37 +/- 0.13
       name: mean_reward
       verified: false
 ---
@@ -30,63 +30,8 @@ TODO: Add your code
 
 
 ```python
-import panda_gym
-import gym
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
 
-from huggingface_sb3 import package_to_hub
-
-from stable_baselines3 import A2C
-from stable_baselines3.common.env_util import make_vec_env
-from stable_baselines3.common.vec_env import SubprocVecEnv
-from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
-from stable_baselines3.common.evaluation import evaluate_policy
-
-env_id = "PandaReachDense-v2"
-model_name = "PandaReachDenseA2C-n8"
-env_name = f"{env_id}_vec_normalize.pkl"
-
-if __name__=="__main__":
-    env = make_vec_env(env_id, n_envs=6, vec_env_cls=SubprocVecEnv)
-    # 3
-    env = VecNormalize(env, norm_obs=True, norm_reward=False, clip_obs=10.)
-    def linear_scheduler(progress_remaining: float):
-        # from https://github.com/DLR-RM/rl-baselines3-zoo/blob/33eba22eb36128412a5b22b57a7a10bfe71e6278/rl_zoo3/utils.py
-        return progress_remaining * 0.0009
-    # 4
-    model = A2C(policy = "MultiInputPolicy",
-            env = env,
-            verbose=1,
-            device='cpu',
-            learning_rate=linear_scheduler,
-            use_rms_prop=True,
-            gae_lambda=0.9,
-            use_sde=True,
-            n_steps=8,
-            )
-    # 5
-    model.learn(1_500_000)
-
-    model.save(model_name)
-    env.save(env_name)
-    del env
-
-    eval_env = DummyVecEnv([lambda: gym.make("PandaReachDense-v2")])
-    eval_env = VecNormalize.load(env_name, eval_env)
-
-    eval_env.training = False
-    eval_env.norm_reward = False
-
-    mean_reward, std_reward = evaluate_policy(model, eval_env)
-    print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
-    
-    package_to_hub(
-        model=model,
-        model_name=model_name,
-        model_architecture="A2C",
-        env_id=env_id,
-        eval_env=eval_env,
-        repo_id=f"bobobert4/a2c-{env_id}",
-        commit_message="Another commit",
-    )
 ...
 ```

config.json

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It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f9c34c840d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9c34d03540>"}, "verbose": 1, 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