Model commit: PandaPickAndPlaceSAC-n2

PandaPickAndPlaceSAC-n2.zip

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+        "__doc__": "\n    Policy class (with both actor and critic) for SAC.\n\n    :param observation_space: Observation space\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 use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n    :param features_extractor_class: Features extractor to use.\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    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
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+        "__module__": "stable_baselines3.common.buffers",
+        "__doc__": "\n    Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n    Extends the ReplayBuffer to use dictionary observations\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
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+}

PandaPickAndPlaceSAC-n2/ent_coef_optimizer.pth

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PandaPickAndPlaceSAC-n2/policy.pth

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PandaPickAndPlaceSAC-n2/pytorch_variables.pth

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PandaPickAndPlaceSAC-n2/system_info.txt

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+- 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: PandaPickAndPlaceDense-v2
     metrics:
     - type: mean_reward
-      value: -9.60 +/- 3.16
+      value: -8.84 +/- 3.11
       name: mean_reward
       verified: false
 ---

config.json

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results.json

@@ -1 +1 @@
-{"mean_reward": -9.597975699976086, "std_reward": 3.1557037667789385, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-17T17:33:28.269400"}
+{"mean_reward": -8.83792223520577, "std_reward": 3.1109936271684266, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-17T18:08:48.042379"}

vec_normalize.pkl

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+size 3579