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Browse files- README.md +37 -0
- a2c-PandaReachDense-v3.zip +3 -0
- a2c-PandaReachDense-v3/_stable_baselines3_version +1 -0
- a2c-PandaReachDense-v3/data +97 -0
- a2c-PandaReachDense-v3/policy.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
- 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: 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: -0.18 +/- 0.08
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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:ca67e2f275672d7c496fcf3cb64835a636dea5f1d27ef5a3638d6e6f8e5dd32a
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size 106915
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a2c-PandaReachDense-v3/_stable_baselines3_version
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2.1.0
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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:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
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"__module__": "stable_baselines3.common.policies",
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"__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 ",
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"__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7c9d01a78430>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7c9d01a74c00>"
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},
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"verbose": 1,
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"policy_kwargs": {
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":type:": "<class 'dict'>",
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":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
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"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
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"optimizer_kwargs": {
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"alpha": 0.99,
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"eps": 1e-05,
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"weight_decay": 0
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}
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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": 1692526163749356534,
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"learning_rate": 0.0007,
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"achieved_goal": "[[ 0.9756516 -0.3848894 0.26153487]\n [ 0.23869742 0.0059426 0.43324435]\n [-0.85726976 -0.5228453 -1.2667094 ]\n [ 1.1851281 -1.1869352 -0.9024955 ]]",
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"desired_goal": "[[ 1.354748 -1.3379161 -0.2022985 ]\n [ 0.28183967 0.10935366 -0.36886126]\n [-0.7729485 -0.26447293 -1.4817176 ]\n [ 1.2567296 -0.9934417 -1.0487018 ]]",
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"observation": "[[ 9.7565162e-01 -3.8488939e-01 2.6153487e-01 1.5848795e+00\n -1.5749983e+00 -1.1107453e+00]\n [ 2.3869742e-01 5.9425994e-03 4.3324435e-01 4.7030410e-01\n -1.4813130e-03 3.7862173e-01]\n [-8.5726976e-01 -5.2284533e-01 -1.2667094e+00 -8.6892927e-01\n 6.5218335e-01 -8.7672073e-01]\n [ 1.1851281e+00 -1.1869352e+00 -9.0249550e-01 2.1636301e-01\n -8.4649831e-01 -1.5930910e+00]]"
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},
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"desired_goal": "[[-0.03001449 0.04763183 0.00123324]\n [ 0.04219246 0.12752499 0.24659017]\n [-0.12355885 -0.14929858 0.13759713]\n [-0.02552408 -0.04687341 0.20673518]]",
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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 [ 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": 0,
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results.json
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