Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -9.80 +/- 3.74
+      value: -1.52 +/- 0.33
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

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a2c-PandaReachDense-v2/data

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         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). 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    ",
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lUvC/lIaUUpRoFUsyaBZHQNFJRtLYf4h1fZQoaAZoCWgPQwiT/l4KD3oAwJSGlFKUaBVLMmgWR0DRSUAMLF4tdX2UKGgGaAloD0MI8bkT7L/O8L+UhpRSlGgVSzJoFkdA0UlrAPd2xXV9lChoBmgJaA9DCEzHnGfsS/q/lIaUUpRoFUsyaBZHQNFJZApnYg91fZQoaAZoCWgPQwgO2xZlNgj4v5SGlFKUaBVLMmgWR0DRSV1uFYdRdX2UKGgGaAloD0MIqrncYKgD+7+UhpRSlGgVSzJoFkdA0UlWpaA4GXV9lChoBmgJaA9DCEfmkT8YeOy/lIaUUpRoFUsyaBZHQNFJgc+JP691fZQoaAZoCWgPQwhHBU62gfv4v5SGlFKUaBVLMmgWR0DRSXrZFocrdX2UKGgGaAloD0MIVUyln3C297+UhpRSlGgVSzJoFkdA0Ul0O5avBHV9lChoBmgJaA9DCKq6RzZXzfy/lIaUUpRoFUsyaBZHQNFJbXG4qgB1ZS4="
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 500000,
-    "n_steps": 5,
+    "_n_updates": 312500,
+    "n_steps": 8,
     "gamma": 0.99,
-    "gae_lambda": 1.0,
+    "gae_lambda": 0.9,
     "ent_coef": 0.0,
-    "vf_coef": 0.5,
+    "vf_coef": 0.4,
     "max_grad_norm": 0.5,
     "normalize_advantage": false
 }

a2c-PandaReachDense-v2/policy.optimizer.pth

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a2c-PandaReachDense-v2/policy.pth

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