Initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -6,7 +6,7 @@ tags:
 - reinforcement-learning
 - stable-baselines3
 model-index:
-- name: A2C
+- name: TQC
   results:
   - task:
       type: reinforcement-learning
@@ -16,13 +16,13 @@ model-index:
       type: PandaPickAndPlace-v3
     metrics:
     - type: mean_reward
-      value: -26.60 +/- 19.93
+      value: -5.80 +/- 3.16
       name: mean_reward
       verified: false
 ---
 
-# **A2C** Agent playing **PandaPickAndPlace-v3**
-This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
+# **TQC** Agent playing **PandaPickAndPlace-v3**
+This is a trained model of a **TQC** agent playing **PandaPickAndPlace-v3**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)

config.json

@@ -1 +1 @@
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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    ", "__init__": "<function MultiInputPolicy.__init__ at 0x7e1bf110d7e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e1bf1107040>"}, "verbose": 1, "policy_kwargs": {"net_arch": [512, 512, 512], "n_critics": 2, 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element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param env: The training environment\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    :param n_sampled_goal: Number of virtual transitions to create per real transition,\n        by sampling new goals.\n    :param goal_selection_strategy: Strategy for sampling goals for replay.\n        One of ['episode', 'final', 'future']\n    :param copy_info_dict: Whether to copy the info dictionary and pass it to\n        ``compute_reward()`` method.\n        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results.json

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+    "batch_size": 2048,
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+    "tau": 0.05,
+    "gamma": 0.95,
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+        "__annotations__": "{'env': typing.Optional[stable_baselines3.common.vec_env.base_vec_env.VecEnv]}",
+        "__doc__": "\n    Hindsight Experience Replay (HER) buffer.\n    Paper: https://arxiv.org/abs/1707.01495\n\n    Replay buffer for sampling HER (Hindsight Experience Replay) transitions.\n\n    .. note::\n\n      Compared to other implementations, the ``future`` goal sampling strategy is inclusive:\n      the current transition can be used when re-sampling.\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 env: The training environment\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    :param n_sampled_goal: Number of virtual transitions to create per real transition,\n        by sampling new goals.\n    :param goal_selection_strategy: Strategy for sampling goals for replay.\n        One of ['episode', 'final', 'future']\n    :param copy_info_dict: Whether to copy the info dictionary and pass it to\n        ``compute_reward()`` method.\n        Please note that the copy may cause a slowdown.\n        False by default.\n    ",
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+        "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x715a77fe5ea0>",
+        "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x715a77fe5f30>",
+        "set_env": "<function HerReplayBuffer.set_env at 0x715a77fe5fc0>",
+        "add": "<function HerReplayBuffer.add at 0x715a77fe6050>",
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+        "sample": "<function HerReplayBuffer.sample at 0x715a77fe6170>",
+        "_get_real_samples": "<function HerReplayBuffer._get_real_samples at 0x715a77fe6200>",
+        "_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x715a77fe6290>",
+        "_sample_goals": "<function HerReplayBuffer._sample_goals at 0x715a77fe6320>",
+        "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x715a77fe63b0>",
+        "__abstractmethods__": "frozenset()",
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+    },
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+    "ent_coef": "auto",
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+    "top_quantiles_to_drop_per_net": 2,
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+    "batch_norm_stats_target": []
+}

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