Initial commit
Browse files- README.md +37 -0
- SACNew-PandaReachDense-v3.zip +3 -0
- SACNew-PandaReachDense-v3/_stable_baselines3_version +1 -0
- SACNew-PandaReachDense-v3/actor.optimizer.pth +3 -0
- SACNew-PandaReachDense-v3/critic.optimizer.pth +3 -0
- SACNew-PandaReachDense-v3/data +114 -0
- SACNew-PandaReachDense-v3/ent_coef_optimizer.pth +3 -0
- SACNew-PandaReachDense-v3/policy.pth +3 -0
- SACNew-PandaReachDense-v3/pytorch_variables.pth +3 -0
- SACNew-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: SAC
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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: -0.17 +/- 0.09
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name: mean_reward
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verified: false
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---
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# **SAC** Agent playing **PandaReachDense-v3**
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This is a trained model of a **SAC** 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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SACNew-PandaReachDense-v3.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:9b3b1cd8c7c3a4e4a9b55ff15d5b6408d0372dc41535e2e3775adc00cb41e817
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size 3143046
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SACNew-PandaReachDense-v3/_stable_baselines3_version
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2.3.2
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SACNew-PandaReachDense-v3/actor.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:1faeb780ad7f7f1a27b9b5e71579c3e1b0e3888ba7ef763065c4def7749af75a
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size 572238
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SACNew-PandaReachDense-v3/critic.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:87c1b10daee27fea08379fb083337d6868499dafedbdd828a3a259b5edea53bd
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size 1132458
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SACNew-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:": "gAWVNwAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu",
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"__module__": "stable_baselines3.sac.policies",
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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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"__init__": "<function MultiInputPolicy.__init__ at 0x7f7754e870a0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7f7754e8b080>"
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},
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"verbose": 1,
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"policy_kwargs": {
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"use_sde": false
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},
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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": 1717186337225008350,
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"learning_rate": 0.01,
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"tensorboard_log": null,
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"_last_obs": {
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"achieved_goal": "[[ 0.35403863 -0.00157899 0.48785 ]\n [ 0.35403863 -0.00157899 0.48785 ]\n [ 0.35403863 -0.00157899 0.48785 ]\n [-0.1002264 -0.5940921 -0.23439904]]",
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"desired_goal": "[[-0.90718865 0.23605156 -1.3270087 ]\n [ 0.52650684 -0.6221669 -0.8289831 ]\n [-1.1058753 1.0090717 1.3603959 ]\n [-1.3681539 -1.0048281 -0.55470353]]",
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"observation": "[[ 3.5403863e-01 -1.5789933e-03 4.8785001e-01 5.6143987e-01\n 4.2719184e-03 3.8781813e-01]\n [ 3.5403863e-01 -1.5789933e-03 4.8785001e-01 5.6143987e-01\n 4.2719184e-03 3.8781813e-01]\n [ 3.5403863e-01 -1.5789933e-03 4.8785001e-01 5.6143987e-01\n 4.2719184e-03 3.8781813e-01]\n [-1.0022640e-01 -5.9409207e-01 -2.3439904e-01 -1.9394903e+00\n -1.9677166e+00 -1.3742864e+00]]"
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},
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"_last_episode_starts": {
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":type:": "<class 'numpy.ndarray'>",
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"achieved_goal": "[[ 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]\n [ 1.7975783e-02 -2.5128206e-02 1.6754021e-01]]",
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"desired_goal": "[[-0.09180731 0.02124441 0.0205898 ]\n [ 0.04008994 -0.05648061 0.06599796]\n [-0.11008611 0.09125341 0.26561755]\n [-0.13421524 -0.09113653 0.09100577]]",
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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 [ 3.8439669e-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 [ 1.7975783e-02 -2.5128206e-02 1.6754021e-01 -9.6544343e-01\n -1.2380710e+00 -1.1582320e+00]]"
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},
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"_episode_num": 342599,
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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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SACNew-PandaReachDense-v3/ent_coef_optimizer.pth
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ADDED
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- Python: 3.10.12
|
3 |
+
- Stable-Baselines3: 2.3.2
|
4 |
+
- PyTorch: 2.3.0+cu121
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.25.2
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.29.1
|
9 |
+
- OpenAI Gym: 0.25.2
|
config.json
ADDED
@@ -0,0 +1 @@
|
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|
|
|
1 |
+
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replay.mp4
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Binary file (684 kB). View file
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results.json
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{"mean_reward": -0.16694701462984085, "std_reward": 0.0946994147881498, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-31T21:51:16.984412"}
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vec_normalize.pkl
ADDED
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size 2849
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