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Browse files- README.md +37 -0
- a2c-PandaPickAndPlaceDense-v3.zip +3 -0
- a2c-PandaPickAndPlaceDense-v3/_stable_baselines3_version +1 -0
- a2c-PandaPickAndPlaceDense-v3/data +101 -0
- a2c-PandaPickAndPlaceDense-v3/policy.optimizer.pth +3 -0
- a2c-PandaPickAndPlaceDense-v3/policy.pth +3 -0
- a2c-PandaPickAndPlaceDense-v3/pytorch_variables.pth +3 -0
- a2c-PandaPickAndPlaceDense-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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- PandaPickAndPlaceDense-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: PandaPickAndPlaceDense-v3
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type: PandaPickAndPlaceDense-v3
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metrics:
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- type: mean_reward
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value: -50.00 +/- 0.00
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name: mean_reward
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verified: false
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---
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# **A2C** Agent playing **PandaPickAndPlaceDense-v3**
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This is a trained model of a **A2C** agent playing **PandaPickAndPlaceDense-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-PandaPickAndPlaceDense-v3.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:2bca8e9c03d5f653fbf475369a409d234fce287ad393d9d5c99e97d453ce3be6
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size 4464711
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a2c-PandaPickAndPlaceDense-v3/_stable_baselines3_version
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2.1.0
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a2c-PandaPickAndPlaceDense-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 0x7e9d1881fc70>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x7e9d18820800>"
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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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"net_arch": [
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512,
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512
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],
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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": 1700027643571263479,
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"learning_rate": 0.0001,
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"achieved_goal": "[[ 1.458027 0.04671702 0.10047653]\n [ 1.1133938 0.40660512 0.10046632]\n [-0.20651145 -0.5339635 0.10047068]\n [-0.1479058 0.09368523 0.10045364]]",
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},
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"_last_episode_starts": {
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"desired_goal": "[[-0.13891526 0.02592641 0.1158672 ]\n [-0.09378273 -0.13938835 0.09003171]\n [-0.13509369 0.08097841 0.1309422 ]\n [-0.11127503 0.13922402 0.02 ]]",
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},
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replay.mp4
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Binary file (762 kB). View file
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results.json
ADDED
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1 |
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{"mean_reward": -50.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-15T06:45:41.240162"}
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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 sha256:1c773a331078ffe8a3d0df8ecb2b7450ca9e7bc4e7b24ae3c04b676e04cec919
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size 3013
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