Initial commit

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,67 @@
+---
+library_name: stable-baselines3
+tags:
+- FetchPickAndPlace-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: TQC
+  results:
+  - metrics:
+    - type: mean_reward
+      value: -8.50 +/- 3.47
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: FetchPickAndPlace-v1
+      type: FetchPickAndPlace-v1
+---
+
+# **TQC** Agent playing **FetchPickAndPlace-v1**
+This is a trained model of a **TQC** agent playing **FetchPickAndPlace-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
+
+```
+# Download model and save it into the logs/ folder
+python -m utils.load_from_hub --algo tqc --env FetchPickAndPlace-v1 -orga sb3 -f logs/
+python enjoy.py --algo tqc --env FetchPickAndPlace-v1  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python train.py --algo tqc --env FetchPickAndPlace-v1 -f logs/
+# Upload the model and generate video (when possible)
+python -m utils.push_to_hub --algo tqc --env FetchPickAndPlace-v1 -f logs/ -orga sb3
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 512),
+             ('buffer_size', 1000000),
+             ('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
+             ('gamma', 0.98),
+             ('learning_rate', 0.001),
+             ('n_timesteps', 1000000.0),
+             ('policy', 'MultiInputPolicy'),
+             ('policy_kwargs', 'dict(net_arch=[512, 512, 512], n_critics=2)'),
+             ('replay_buffer_class', 'HerReplayBuffer'),
+             ('replay_buffer_kwargs',
+              "dict( online_sampling=True, goal_selection_strategy='future', "
+              'n_sampled_goal=4, max_episode_length=100 )'),
+             ('tau', 0.005),
+             ('normalize', False)])
+```

args.yml

@@ -0,0 +1,63 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - tqc
+  - - env
+    - FetchPickAndPlace-v1
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 20
+  - - eval_freq
+    - 50000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - logs
+  - - log_interval
+    - -1
+  - - n_eval_envs
+    - 5
+  - - n_evaluations
+    - 20
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - -1
+  - - n_trials
+    - 10
+  - - no_optim_plots
+    - false
+  - - num_threads
+    - 2
+  - - optimize_hyperparameters
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 1238172901
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - ''
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1

config.yml

@@ -0,0 +1,24 @@
+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 512
+  - - buffer_size
+    - 1000000
+  - - env_wrapper
+    - sb3_contrib.common.wrappers.TimeFeatureWrapper
+  - - gamma
+    - 0.98
+  - - learning_rate
+    - 0.001
+  - - n_timesteps
+    - 1000000.0
+  - - policy
+    - MultiInputPolicy
+  - - policy_kwargs
+    - dict(net_arch=[512, 512, 512], n_critics=2)
+  - - replay_buffer_class
+    - HerReplayBuffer
+  - - replay_buffer_kwargs
+    - dict( online_sampling=True, goal_selection_strategy='future', n_sampled_goal=4,
+      max_episode_length=100 )
+  - - tau
+    - 0.005

env_kwargs.yml

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+{}

replay.mp4

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+size 594941

results.json

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+{"mean_reward": -8.5, "std_reward": 3.4713109915419564, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-02T22:58:29.464303"}

tqc-FetchPickAndPlace-v1.zip

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tqc-FetchPickAndPlace-v1/_stable_baselines3_version

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+1.5.1a8

tqc-FetchPickAndPlace-v1/actor.optimizer.pth

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tqc-FetchPickAndPlace-v1/critic.optimizer.pth

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tqc-FetchPickAndPlace-v1/data

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+    "batch_size": 512,
+    "learning_starts": 100,
+    "tau": 0.005,
+    "gamma": 0.98,
+    "gradient_steps": 1,
+    "optimize_memory_usage": false,
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+        "__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 0x7fa39794fb90>",
+        "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x7fa39794fc20>",
+        "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x7fa39794fcb0>",
+        "set_env": "<function HerReplayBuffer.set_env at 0x7fa39794fd40>",
+        "_get_samples": "<function HerReplayBuffer._get_samples at 0x7fa39794fdd0>",
+        "sample": "<function HerReplayBuffer.sample at 0x7fa39794fe60>",
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+        "sample_goals": "<function HerReplayBuffer.sample_goals at 0x7fa39794ff80>",
+        "_sample_transitions": "<function HerReplayBuffer._sample_transitions at 0x7fa3974c6050>",
+        "add": "<function HerReplayBuffer.add at 0x7fa3974c60e0>",
+        "store_episode": "<function HerReplayBuffer.store_episode at 0x7fa3974c6170>",
+        "_sample_her_transitions": "<function HerReplayBuffer._sample_her_transitions at 0x7fa3974c6200>",
+        "n_episodes_stored": "<property object at 0x7fa3979523b0>",
+        "size": "<function HerReplayBuffer.size at 0x7fa3974c6320>",
+        "reset": "<function HerReplayBuffer.reset at 0x7fa3974c63b0>",
+        "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x7fa3974c6440>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fa397950210>"
+    },
+    "replay_buffer_kwargs": {
+        "online_sampling": true,
+        "goal_selection_strategy": "future",
+        "n_sampled_goal": 4,
+        "max_episode_length": 100
+    },
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+    },
+    "use_sde_at_warmup": false,
+    "target_entropy": -4.0,
+    "ent_coef": "auto",
+    "target_update_interval": 1,
+    "top_quantiles_to_drop_per_net": 2,
+    "remove_time_limit_termination": false
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