Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: TD3
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.21 +/- 0.12
+      name: mean_reward
+      verified: false
+---
+
+# **TD3** Agent playing **PandaReachDense-v3**
+This is a trained model of a **TD3** agent playing **PandaReachDense-v3**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNwAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnRkMy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", "__module__": "stable_baselines3.td3.policies", "__doc__": "\n    Policy class (with both actor and critic) for TD3 to be used with Dict observation spaces.\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 features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 0x78348c8f8280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78348c8f0940>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1717790145331240139, "learning_rate": 0.001, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-1.0627693  -0.91510946 -1.5850064 ]\n [ 0.30097526  0.02901173  0.37628466]\n [ 0.30097526  0.02901173  0.37628466]\n [ 0.30097526  0.02901173  0.37628466]]", "desired_goal": "[[-0.7228617  -0.35448352 -1.3393947 ]\n [ 1.6487684  -1.5895358   0.17337722]\n [ 1.4678886  -1.3285404   0.8752596 ]\n [-0.27272767  1.2635647  -0.77433074]]", "observation": "[[-1.0627693  -0.91510946 -1.5850064  -0.9280248   0.37157795 -0.92570984]\n [ 0.30097526  0.02901173  0.37628466  0.5798172  -0.00755449  0.4156822 ]\n [ 0.30097526  0.02901173  0.37628466  0.5798172  -0.00755449  0.4156822 ]\n [ 0.30097526  0.02901173  0.37628466  0.5798172  -0.00755449  0.4156822 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-2.3753852e-02 -4.0998604e-02  1.1316971e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[-0.07329826 -0.03245217  0.01901941]\n [ 0.14354414 -0.14444758  0.15799384]\n [ 0.12700596 -0.12078033  0.22247395]\n [-0.0321417   0.11427356  0.07093037]]", "observation": "[[-2.3753852e-02 -4.0998604e-02  1.1316971e-01 -6.2445706e-01\n   2.5743738e-01 -9.2578644e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 375296, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVhgAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhlLg=="}, "_n_updates": 249975, "buffer_size": 1000000, "batch_size": 256, "learning_starts": 100, "tau": 0.005, "gamma": 0.99, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwQRGljdFJlcGxheUJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.common.buffers", "__annotations__": "{'observation_space': <class 'gymnasium.spaces.dict.Dict'>, 'obs_shape': typing.Dict[str, typing.Tuple[int, ...]], 'observations': typing.Dict[str, numpy.ndarray], 'next_observations': typing.Dict[str, numpy.ndarray]}", "__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    ", "__init__": "<function DictReplayBuffer.__init__ at 0x78348ca30040>", "add": "<function DictReplayBuffer.add at 0x78348ca300d0>", "sample": "<function DictReplayBuffer.sample at 0x78348ca30160>", "_get_samples": "<function DictReplayBuffer._get_samples at 0x78348ca301f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78348ca27880>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "policy_delay": 2, "target_noise_clip": 0.5, "target_policy_noise": 0.2, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": "Generator(PCG64)"}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "actor_batch_norm_stats": [], "critic_batch_norm_stats": [], "actor_batch_norm_stats_target": [], "critic_batch_norm_stats_target": [], "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.3.2", "PyTorch": "2.3.0+cu121", "GPU Enabled": "False", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.21012458456680178, "std_reward": 0.12106678277381835, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-08T01:12:49.868099"}

td3-PandaReachDense-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb72743c2487eb2dfbd36958a0095cbde1611bfa6146c8ec667843bdefaeaef0
+size 6132395

td3-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.3.2

td3-PandaReachDense-v3/actor.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43536099b7b86e74e5f6ec98b4537502e769fee36cbfba7e774ae38083324b0a
+size 1016352

td3-PandaReachDense-v3/critic.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d2b15b88093af6f3271e19320a2208ca7706a78f4b5723fe568d094c208d5b0
+size 2041322

td3-PandaReachDense-v3/data

@@ -0,0 +1,114 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVNwAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnRkMy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu",
+        "__module__": "stable_baselines3.td3.policies",
+        "__doc__": "\n    Policy class (with both actor and critic) for TD3 to be used with Dict observation spaces.\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 features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 0x78348c8f8280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78348c8f0940>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1717790145331240139,
+    "learning_rate": 0.001,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-1.0627693  -0.91510946 -1.5850064 ]\n [ 0.30097526  0.02901173  0.37628466]\n [ 0.30097526  0.02901173  0.37628466]\n [ 0.30097526  0.02901173  0.37628466]]",
+        "desired_goal": "[[-0.7228617  -0.35448352 -1.3393947 ]\n [ 1.6487684  -1.5895358   0.17337722]\n [ 1.4678886  -1.3285404   0.8752596 ]\n [-0.27272767  1.2635647  -0.77433074]]",
+        "observation": "[[-1.0627693  -0.91510946 -1.5850064  -0.9280248   0.37157795 -0.92570984]\n [ 0.30097526  0.02901173  0.37628466  0.5798172  -0.00755449  0.4156822 ]\n [ 0.30097526  0.02901173  0.37628466  0.5798172  -0.00755449  0.4156822 ]\n [ 0.30097526  0.02901173  0.37628466  0.5798172  -0.00755449  0.4156822 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-2.3753852e-02 -4.0998604e-02  1.1316971e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[-0.07329826 -0.03245217  0.01901941]\n [ 0.14354414 -0.14444758  0.15799384]\n [ 0.12700596 -0.12078033  0.22247395]\n [-0.0321417   0.11427356  0.07093037]]",
+        "observation": "[[-2.3753852e-02 -4.0998604e-02  1.1316971e-01 -6.2445706e-01\n   2.5743738e-01 -9.2578644e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 375296,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVhgAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhlLg=="
+    },
+    "_n_updates": 249975,
+    "buffer_size": 1000000,
+    "batch_size": 256,
+    "learning_starts": 100,
+    "tau": 0.005,
+    "gamma": 0.99,
+    "gradient_steps": 1,
+    "optimize_memory_usage": false,
+    "replay_buffer_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwQRGljdFJlcGxheUJ1ZmZlcpSTlC4=",
+        "__module__": "stable_baselines3.common.buffers",
+        "__annotations__": "{'observation_space': <class 'gymnasium.spaces.dict.Dict'>, 'obs_shape': typing.Dict[str, typing.Tuple[int, ...]], 'observations': typing.Dict[str, numpy.ndarray], 'next_observations': typing.Dict[str, numpy.ndarray]}",
+        "__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    ",
+        "__init__": "<function DictReplayBuffer.__init__ at 0x78348ca30040>",
+        "add": "<function DictReplayBuffer.add at 0x78348ca300d0>",
+        "sample": "<function DictReplayBuffer.sample at 0x78348ca30160>",
+        "_get_samples": "<function DictReplayBuffer._get_samples at 0x78348ca301f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78348ca27880>"
+    },
+    "replay_buffer_kwargs": {},
+    "train_freq": {
+        ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
+        ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
+    },
+    "use_sde_at_warmup": false,
+    "policy_delay": 2,
+    "target_noise_clip": 0.5,
+    "target_policy_noise": 0.2,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": "Generator(PCG64)"
+    },
+    "n_envs": 4,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "actor_batch_norm_stats": [],
+    "critic_batch_norm_stats": [],
+    "actor_batch_norm_stats_target": [],
+    "critic_batch_norm_stats_target": []
+}

td3-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac56bcc4b1411e810eff26660b928f1ca3a41f0a242558ff3077cb6543bb9a83
+size 3056234

td3-PandaReachDense-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

td3-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.3.2
+- PyTorch: 2.3.0+cu121
+- GPU Enabled: False
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.25.2

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb3fae4ff730a66e8abe0f86bc7c6da541128e04550c0062ff7446edff1527a3
+size 2847