Initial commit
Browse files- README.md +37 -0
- a2c-PandaReachDense-v3.zip +3 -0
- a2c-PandaReachDense-v3/_stable_baselines3_version +1 -0
- a2c-PandaReachDense-v3/actor.optimizer.pth +3 -0
- a2c-PandaReachDense-v3/critic.optimizer.pth +3 -0
- a2c-PandaReachDense-v3/data +114 -0
- a2c-PandaReachDense-v3/ent_coef_optimizer.pth +3 -0
- a2c-PandaReachDense-v3/policy.pth +3 -0
- a2c-PandaReachDense-v3/pytorch_variables.pth +3 -0
- a2c-PandaReachDense-v3/system_info.txt +9 -0
- config.json +1 -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: A2C
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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: -0.12 +/- 0.11
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name: mean_reward
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verified: false
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---
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# **A2C** Agent playing **PandaReachDense-v3**
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This is a trained model of a **A2C** 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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a2c-PandaReachDense-v3.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fe6e29efbef23514f70d8584a922347623f286bc8c66dff19d23c53789e8a45
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size 3141749
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a2c-PandaReachDense-v3/_stable_baselines3_version
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2.3.2
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a2c-PandaReachDense-v3/actor.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:fc98cb201569b0e2316006f183ce87790edd54b17958628d7919d8da0bb5c0b6
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size 571982
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a2c-PandaReachDense-v3/critic.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:1d7441af48da3010174efe56278a7c4d502b1c332846d637019d59694f4820ab
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size 1131946
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a2c-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 0x7e1d838b79a0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7e1d836c3f40>"
|
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},
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"verbose": 1,
|
| 12 |
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"policy_kwargs": {
|
| 13 |
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"use_sde": false
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},
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| 15 |
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"num_timesteps": 1000000,
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| 16 |
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"_total_timesteps": 1000000,
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"_num_timesteps_at_start": 0,
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| 18 |
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"seed": null,
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"action_noise": null,
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"start_time": 1717678889523985754,
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"learning_rate": 0.0003,
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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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":serialized:": "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",
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"achieved_goal": "[[ 0.48593724 0.41881633 0.8182322 ]\n [ 0.32318288 -0.06420207 0.53251487]\n [ 0.32318288 -0.06420207 0.53251487]\n [ 0.32318288 -0.06420207 0.53251487]]",
|
| 27 |
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"desired_goal": "[[ 0.6753942 0.8989489 1.0297809 ]\n [ 0.7468885 0.1281578 -0.5890698 ]\n [ 0.39018017 -0.34852502 0.883982 ]\n [-0.3158677 0.18268321 -1.1175094 ]]",
|
| 28 |
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"observation": "[[ 0.48593724 0.41881633 0.8182322 1.2937069 1.922245 1.4677873 ]\n [ 0.32318288 -0.06420207 0.53251487 0.52614737 -0.01287868 0.3685873 ]\n [ 0.32318288 -0.06420207 0.53251487 0.52614737 -0.01287868 0.3685873 ]\n [ 0.32318288 -0.06420207 0.53251487 0.52614737 -0.01287868 0.3685873 ]]"
|
| 29 |
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},
|
| 30 |
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"_last_episode_starts": {
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| 31 |
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":type:": "<class 'numpy.ndarray'>",
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| 32 |
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":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
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},
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"_last_original_obs": {
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":type:": "<class 'collections.OrderedDict'>",
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":serialized:": "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",
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"achieved_goal": "[[ 4.6467911e-02 2.4478629e-02 2.1392143e-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]]",
|
| 38 |
+
"desired_goal": "[[ 0.05577144 0.08033716 0.2362584 ]\n [ 0.06229192 0.01104843 0.08893096]\n [ 0.02975916 -0.03180202 0.22298962]\n [-0.03463429 0.01594988 0.04083902]]",
|
| 39 |
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"observation": "[[ 4.6467911e-02 2.4478629e-02 2.1392143e-01 2.7916941e-01\n 1.1131716e+00 6.8653351e-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]]"
|
| 40 |
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},
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| 41 |
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"_episode_num": 302180,
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| 42 |
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"use_sde": false,
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| 43 |
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"sde_sample_freq": -1,
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| 44 |
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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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a2c-PandaReachDense-v3/ent_coef_optimizer.pth
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| 1 |
+
- 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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- Python: 3.10.12
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| 3 |
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- Stable-Baselines3: 2.3.2
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| 4 |
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- PyTorch: 2.3.0+cu121
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- GPU Enabled: False
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| 6 |
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- Numpy: 1.25.2
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| 7 |
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- Cloudpickle: 2.2.1
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| 8 |
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- Gymnasium: 0.29.1
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| 9 |
+
- OpenAI Gym: 0.25.2
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config.json
ADDED
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results.json
ADDED
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@@ -0,0 +1 @@
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{"mean_reward": -0.12428679652512073, "std_reward": 0.10968941474061548, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-06T16:42:42.387509"}
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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:e4b20b2faa356f41222987d51e0182ab3cf82090825a143c04c6c4b175505894
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| 3 |
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size 2847
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