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Browse files- README.md +37 -0
- TD3-PandaReachDense-v3.zip +3 -0
- TD3-PandaReachDense-v3/_stable_baselines3_version +1 -0
- TD3-PandaReachDense-v3/actor.optimizer.pth +3 -0
- TD3-PandaReachDense-v3/critic.optimizer.pth +3 -0
- TD3-PandaReachDense-v3/data +114 -0
- TD3-PandaReachDense-v3/policy.pth +3 -0
- TD3-PandaReachDense-v3/pytorch_variables.pth +3 -0
- TD3-PandaReachDense-v3/system_info.txt +9 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
- vec_normalize.pkl +3 -0
README.md
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---
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library_name: stable-baselines3
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tags:
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- PandaReachDense-v3
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: TD3
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: PandaReachDense-v3
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type: PandaReachDense-v3
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metrics:
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- type: mean_reward
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value: -0.20 +/- 0.11
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name: mean_reward
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verified: false
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---
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# **TD3** Agent playing **PandaReachDense-v3**
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This is a trained model of a **TD3** agent playing **PandaReachDense-v3**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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TODO: Add your code
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```python
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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...
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```
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TD3-PandaReachDense-v3.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:f6854928dbd30bd31190f643bafd3fcba5ca63c9216cbf3c785cb166b290953b
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size 6133546
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TD3-PandaReachDense-v3/_stable_baselines3_version
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2.3.2
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TD3-PandaReachDense-v3/actor.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:a8ff2c41cf8fccb8b55145d35c0d0f7f39ab47f1fecfbba3bf6047e1425c0fb0
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size 1016544
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TD3-PandaReachDense-v3/critic.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:1f6088886da93c8396df1d73070985d8187125f951373bf4f13df36c74c96642
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size 2041834
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TD3-PandaReachDense-v3/data
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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":serialized:": "gAWVNwAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnRkMy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu",
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"__module__": "stable_baselines3.td3.policies",
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"__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 ",
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"__init__": "<function MultiInputPolicy.__init__ at 0x7a04648bd870>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7a04648c5200>"
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},
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"verbose": 1,
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"policy_kwargs": {},
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"num_timesteps": 1000000,
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"_total_timesteps": 1000000,
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"_num_timesteps_at_start": 0,
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"seed": null,
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"action_noise": null,
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"start_time": 1717178474287710760,
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"learning_rate": 0.001,
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"tensorboard_log": null,
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"_last_obs": {
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":type:": "<class 'collections.OrderedDict'>",
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"achieved_goal": "[[ 0.32731283 0.04577133 0.42400637]\n [-1.3275453 -1.6050563 0.20288211]\n [-0.1255108 -0.4815829 -0.3517548 ]\n [ 0.32731283 0.04577133 0.42400637]]",
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"desired_goal": "[[ 1.1476855 1.3051419 -0.21491568]\n [-1.0676976 -1.4758599 0.41453058]\n [-0.8036953 -0.7783772 -1.3498782 ]\n [-0.1364327 -0.22785343 1.2785269 ]]",
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"observation": "[[ 0.32731283 0.04577133 0.42400637 0.57013327 0.00624212 0.40765837]\n [-1.3275453 -1.6050563 0.20288211 -0.5751514 -1.1083626 -0.00998895]\n [-0.1255108 -0.4815829 -0.3517548 -2.012644 -1.7471368 -1.4130548 ]\n [ 0.32731283 0.04577133 0.42400637 0.57013327 0.00624212 0.40765837]]"
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},
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"_last_episode_starts": {
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},
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"achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [-3.3318743e-02 -7.8345612e-02 1.8850973e-01]\n [ 1.8804207e-02 -2.5027379e-02 1.6621029e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
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"desired_goal": "[[ 0.09750769 0.11852269 0.12230511]\n [-0.1046747 -0.13352755 0.18012473]\n [-0.08058108 -0.07031267 0.01804983]\n [-0.01968472 -0.02041711 0.25948963]]",
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"observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [-3.3318743e-02 -7.8345612e-02 1.8850973e-01 -4.8227677e-01\n -7.5313902e-01 -2.9008037e-01]\n [ 1.8804207e-02 -2.5027379e-02 1.6621029e-01 -1.0876017e+00\n -1.1847591e+00 -1.2645912e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
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},
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"_episode_num": 375437,
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"use_sde": false,
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"sde_sample_freq": -1,
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"_current_progress_remaining": 0.0,
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"_stats_window_size": 100,
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"ep_info_buffer": {
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":type:": "<class 'collections.deque'>",
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TD3-PandaReachDense-v3/policy.pth
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TD3-PandaReachDense-v3/pytorch_variables.pth
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version https://git-lfs.github.com/spec/v1
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ADDED
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- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
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replay.mp4
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Binary file (644 kB). View file
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results.json
ADDED
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{"mean_reward": -0.20277860471978784, "std_reward": 0.10529909793194796, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-31T19:20:38.101220"}
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vec_normalize.pkl
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:8abb24775bdd84f3f2303b952fdbddd499b03d069e03fdfa5075e9a2cd2d25bb
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size 2854
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