Initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,83 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaSlide-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: TQC
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaSlide-v1
+      type: PandaSlide-v1
+    metrics:
+    - type: mean_reward
+      value: -50.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **TQC** Agent playing **PandaSlide-v1**
+This is a trained model of a **TQC** agent playing **PandaSlide-v1**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
+and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
+
+The RL Zoo is a training framework for Stable Baselines3
+reinforcement learning agents,
+with hyperparameter optimization and pre-trained agents included.
+
+## Usage (with SB3 RL Zoo)
+
+RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
+SB3: https://github.com/DLR-RM/stable-baselines3<br/>
+SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+
+Install the RL Zoo (with SB3 and SB3-Contrib):
+```bash
+pip install rl_zoo3
+```
+
+```
+# Download model and save it into the logs/ folder
+python -m rl_zoo3.load_from_hub --algo tqc --env PandaSlide-v1 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo tqc --env PandaSlide-v1  -f logs/
+```
+
+If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
+```
+python -m rl_zoo3.load_from_hub --algo tqc --env PandaSlide-v1 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo tqc --env PandaSlide-v1  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo tqc --env PandaSlide-v1 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo tqc --env PandaSlide-v1 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 2048),
+             ('buffer_size', 1000000),
+             ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
+             ('gamma', 0.95),
+             ('learning_rate', 0.001),
+             ('n_timesteps', 3000000.0),
+             ('policy', 'MultiInputPolicy'),
+             ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
+             ('replay_buffer_class', 'HerReplayBuffer'),
+             ('replay_buffer_kwargs',
+              "dict( goal_selection_strategy='future', n_sampled_goal=4, )"),
+             ('tau', 0.05),
+             ('normalize', False)])
+```
+
+# Environment Arguments
+```python
+{'render': True}
+```

args.yml

@@ -0,0 +1,83 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - tqc
+  - - conf_file
+    - null
+  - - device
+    - auto
+  - - env
+    - PandaSlide-v1
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 20
+  - - eval_freq
+    - 25000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - logs
+  - - log_interval
+    - -1
+  - - max_total_trials
+    - null
+  - - n_eval_envs
+    - 5
+  - - n_evaluations
+    - null
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - -1
+  - - n_trials
+    - 500
+  - - no_optim_plots
+    - false
+  - - num_threads
+    - -1
+  - - optimization_log_path
+    - null
+  - - optimize_hyperparameters
+    - false
+  - - progress
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 2035054536
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - runs/PandaSlide-v1__tqc__2035054536__1677504380
+  - - track
+    - true
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - qgallouedec
+  - - wandb_project_name
+    - vec-her-sb3
+  - - wandb_tags
+    - []
+  - - yaml_file
+    - null

config.yml

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+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 2048
+  - - buffer_size
+    - 1000000
+  - - env_wrapper
+    - sb3_contrib.common.wrappers.TimeFeatureWrapper
+  - - gamma
+    - 0.95
+  - - learning_rate
+    - 0.001
+  - - n_timesteps
+    - 3000000.0
+  - - policy
+    - MultiInputPolicy
+  - - policy_kwargs
+    - dict(net_arch=[512, 512, 512], n_critics=2)
+  - - replay_buffer_class
+    - HerReplayBuffer
+  - - replay_buffer_kwargs
+    - dict( goal_selection_strategy='future', n_sampled_goal=4, )
+  - - tau
+    - 0.05

env_kwargs.yml

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+render: true

replay.mp4

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results.json

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+{"mean_reward": -50.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-28T17:56:14.197032"}

tqc-PandaSlide-v1.zip

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tqc-PandaSlide-v1/_stable_baselines3_version

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tqc-PandaSlide-v1/actor.optimizer.pth

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tqc-PandaSlide-v1/critic.optimizer.pth

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tqc-PandaSlide-v1/data

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+    "_n_updates": 2999900,
+    "buffer_size": 1,
+    "batch_size": 2048,
+    "learning_starts": 100,
+    "tau": 0.05,
+    "gamma": 0.95,
+    "gradient_steps": 1,
+    "optimize_memory_usage": false,
+    "replay_buffer_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVPwAAAAAAAACMJ3N0YWJsZV9iYXNlbGluZXMzLmhlci5oZXJfcmVwbGF5X2J1ZmZlcpSMD0hlclJlcGxheUJ1ZmZlcpSTlC4=",
+        "__module__": "stable_baselines3.her.her_replay_buffer",
+        "__doc__": "\n    Hindsight Experience Replay (HER) buffer.\n    Paper: https://arxiv.org/abs/1707.01495\n\n    .. warning::\n\n      For performance reasons, the maximum number of steps per episodes must be specified.\n      In most cases, it will be inferred if you specify ``max_episode_steps`` when registering the environment\n      or if you use a ``gym.wrappers.TimeLimit`` (and ``env.spec`` is not None).\n      Otherwise, you can directly pass ``max_episode_length`` to the replay buffer constructor.\n\n\n    Replay buffer for sampling HER (Hindsight Experience Replay) transitions.\n    In the online sampling case, these new transitions will not be saved in the replay buffer\n    and will only be created at sampling time.\n\n    :param env: The training environment\n    :param buffer_size: The size of the buffer measured in transitions.\n    :param max_episode_length: The maximum length of an episode. If not specified,\n        it will be automatically inferred if the environment uses a ``gym.wrappers.TimeLimit`` wrapper.\n    :param goal_selection_strategy: Strategy for sampling goals for replay.\n        One of ['episode', 'final', 'future']\n    :param device: PyTorch device\n    :param n_sampled_goal: Number of virtual transitions to create per real transition,\n        by sampling new goals.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
+        "__init__": "<function HerReplayBuffer.__init__ at 0x7f35bbe78ca0>",
+        "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x7f35bbe78d30>",
+        "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x7f35bbe78dc0>",
+        "set_env": "<function HerReplayBuffer.set_env at 0x7f35bbe78e50>",
+        "_get_samples": "<function HerReplayBuffer._get_samples at 0x7f35bbe78ee0>",
+        "sample": "<function HerReplayBuffer.sample at 0x7f35bbe78f70>",
+        "_sample_offline": "<function HerReplayBuffer._sample_offline at 0x7f35bbe09040>",
+        "sample_goals": "<function HerReplayBuffer.sample_goals at 0x7f35bbe090d0>",
+        "_sample_transitions": "<function HerReplayBuffer._sample_transitions at 0x7f35bbe09160>",
+        "add": "<function HerReplayBuffer.add at 0x7f35bbe091f0>",
+        "store_episode": "<function HerReplayBuffer.store_episode at 0x7f35bbe09280>",
+        "_sample_her_transitions": "<function HerReplayBuffer._sample_her_transitions at 0x7f35bbe09310>",
+        "n_episodes_stored": "<property object at 0x7f35bbe08220>",
+        "size": "<function HerReplayBuffer.size at 0x7f35bbe09430>",
+        "reset": "<function HerReplayBuffer.reset at 0x7f35bbe094c0>",
+        "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x7f35bbe09550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f35bbe0b1c0>"
+    },
+    "replay_buffer_kwargs": {
+        "goal_selection_strategy": "future",
+        "n_sampled_goal": 4
+    },
+    "train_freq": {
+        ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
+        ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
+    },
+    "use_sde_at_warmup": false,
+    "target_entropy": -3.0,
+    "ent_coef": "auto",
+    "target_update_interval": 1,
+    "top_quantiles_to_drop_per_net": 2,
+    "batch_norm_stats": [],
+    "batch_norm_stats_target": []
+}

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+- Python: 3.9.12
+- Stable-Baselines3: 1.8.0a6
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.24.1
+- Gym: 0.21.0

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