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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: -0.20 +/- 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:a447ce3aa48caf98cfd7bf6eed4e1a24df960765d18b907285bc8fc7c4c0f19b
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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 0x7dc27f0884c0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7dc27f07b200>"
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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": 1692805084516606414,
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"learning_rate": 0.0007,
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"tensorboard_log": null,
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"_last_obs": {
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"achieved_goal": "[[-0.041299 0.3846311 -0.22714154]\n [ 0.99618495 1.1735239 1.312861 ]\n [ 0.21522954 -0.00947735 0.4378234 ]\n [-0.12927996 0.38315386 -0.20644695]]",
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"desired_goal": "[[ 0.14687453 0.68622726 -0.2856657 ]\n [ 1.556146 1.3052306 1.5218527 ]\n [ 0.01139857 1.6456002 0.36633384]\n [-0.6156746 1.4713695 -1.5198509 ]]",
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"observation": "[[-4.1299004e-02 3.8463110e-01 -2.2714154e-01 -1.2447083e+00\n 1.6558281e+00 -1.2812049e+00]\n [ 9.9618495e-01 1.1735239e+00 1.3128610e+00 1.1215267e+00\n 8.7139213e-01 1.4880878e+00]\n [ 2.1522954e-01 -9.4773462e-03 4.3782341e-01 4.7837463e-01\n -2.4387502e-04 3.8947305e-01]\n [-1.2927996e-01 3.8315386e-01 -2.0644695e-01 -1.9080424e+00\n 1.6463726e+00 -1.4001048e+00]]"
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},
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"_last_episode_starts": {
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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 [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
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"desired_goal": "[[-0.10608299 0.06561895 0.26656058]\n [-0.02627493 0.04699459 0.13772379]\n [ 0.00412479 0.06329305 0.18071619]\n [ 0.04265098 0.0857816 0.17176212]]",
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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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"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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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__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). 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replay.mp4
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results.json
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vec_normalize.pkl
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version https://git-lfs.github.com/spec/v1
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