Initial commit

.gitattributes

@@ -25,3 +25,5 @@ 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
+vec_normalize.pkl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,97 @@
+---
+library_name: stable-baselines3
+tags:
+- donkey-warren-track-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: TQC
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 175.85 +/- 2.78
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: donkey-warren-track-v0
+      type: donkey-warren-track-v0
+---
+
+# **TQC** Agent playing **donkey-warren-track-v0**
+This is a trained model of a **TQC** agent playing **donkey-warren-track-v0**
+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 donkey-warren-track-v0 -orga araffin -f logs/
+python enjoy.py --algo tqc --env donkey-warren-track-v0  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python train.py --algo tqc --env donkey-warren-track-v0 -f logs/
+# Upload the model and generate video (when possible)
+python -m utils.push_to_hub --algo tqc --env donkey-warren-track-v0 -f logs/ -orga araffin
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 256),
+             ('buffer_size', 200000),
+             ('callback',
+              [{'utils.callbacks.ParallelTrainCallback': {'gradient_steps': 200}},
+               'utils.callbacks.LapTimeCallback']),
+             ('ent_coef', 'auto'),
+             ('env_wrapper',
+              [{'gym.wrappers.time_limit.TimeLimit': {'max_episode_steps': 10000}},
+               'ae.wrapper.AutoencoderWrapper',
+               {'utils.wrappers.HistoryWrapper': {'horizon': 2}}]),
+             ('gamma', 0.99),
+             ('gradient_steps', 256),
+             ('learning_rate', 0.00073),
+             ('learning_starts', 500),
+             ('n_timesteps', 2000000.0),
+             ('normalize', "{'norm_obs': True, 'norm_reward': False}"),
+             ('policy', 'MlpPolicy'),
+             ('policy_kwargs',
+              'dict(log_std_init=-3, net_arch=[256, 256], n_critics=2, '
+              'use_expln=True)'),
+             ('sde_sample_freq', 16),
+             ('tau', 0.02),
+             ('train_freq', 200),
+             ('use_sde', True),
+             ('use_sde_at_warmup', True),
+             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+```
+
+# Environment Arguments
+```python
+{'conf': {'cam_resolution': (120, 160, 3),
+          'car_config': {'body_rgb': (226, 112, 18),
+                         'body_style': 'donkey',
+                         'car_name': 'Toni',
+                         'font_size': 40},
+          'frame_skip': 1,
+          'host': 'localhost',
+          'level': 'warren',
+          'log_level': 20,
+          'max_cte': 8,
+          'port': 9091,
+          'start_delay': 5.0},
+ 'min_throttle': -0.2,
+ 'steer': 0.8}
+```

args.yml

@@ -0,0 +1,96 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - tqc
+  - - device
+    - auto
+  - - env
+    - donkey-warren-track-v0
+  - - env_kwargs
+    - conf:
+        cam_resolution: !!python/tuple
+        - 120
+        - 160
+        - 3
+        car_config:
+          body_rgb: !!python/tuple
+          - 226
+          - 112
+          - 18
+          body_style: donkey
+          car_name: Toni
+          font_size: 40
+        frame_skip: 1
+        host: localhost
+        level: warren
+        log_level: 20
+        max_cte: 8
+        port: 9091
+        start_delay: 5.0
+      min_throttle: -0.2
+      steer: 0.8
+  - - eval_episodes
+    - 5
+  - - eval_freq
+    - -1
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - logs
+  - - log_interval
+    - -1
+  - - max_total_trials
+    - null
+  - - n_eval_envs
+    - 1
+  - - 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
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - 25000
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 2529849244
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - runs/donkey-warren-track-v0__tqc__2529849244__1655634920
+  - - track
+    - true
+  - - trained_agent
+    - logs/tqc/donkey-minimonaco-track-v0_12/donkey-minimonaco-track-v0.zip
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - sb3
+  - - wandb_project_name
+    - donkeycar

config.yml

@@ -0,0 +1,43 @@
+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 256
+  - - buffer_size
+    - 200000
+  - - callback
+    - - utils.callbacks.ParallelTrainCallback:
+          gradient_steps: 200
+      - utils.callbacks.LapTimeCallback
+  - - ent_coef
+    - auto
+  - - env_wrapper
+    - - gym.wrappers.time_limit.TimeLimit:
+          max_episode_steps: 10000
+      - ae.wrapper.AutoencoderWrapper
+      - utils.wrappers.HistoryWrapper:
+          horizon: 2
+  - - gamma
+    - 0.99
+  - - gradient_steps
+    - 256
+  - - learning_rate
+    - 0.00073
+  - - learning_starts
+    - 500
+  - - n_timesteps
+    - 2000000.0
+  - - normalize
+    - '{''norm_obs'': True, ''norm_reward'': False}'
+  - - policy
+    - MlpPolicy
+  - - policy_kwargs
+    - dict(log_std_init=-3, net_arch=[256, 256], n_critics=2, use_expln=True)
+  - - sde_sample_freq
+    - 16
+  - - tau
+    - 0.02
+  - - train_freq
+    - 200
+  - - use_sde
+    - true
+  - - use_sde_at_warmup
+    - true

env_kwargs.yml

@@ -0,0 +1,22 @@
+conf:
+  cam_resolution: !!python/tuple
+  - 120
+  - 160
+  - 3
+  car_config:
+    body_rgb: !!python/tuple
+    - 226
+    - 112
+    - 18
+    body_style: donkey
+    car_name: Toni
+    font_size: 40
+  frame_skip: 1
+  host: localhost
+  level: warren
+  log_level: 20
+  max_cte: 8
+  port: 9091
+  start_delay: 5.0
+min_throttle: -0.2
+steer: 0.8

replay.mp4

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+size 1608142

results.json

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+{"mean_reward": 175.85419530000001, "std_reward": 2.7763180707583968, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-19T13:43:19.584155"}

tqc-donkey-warren-track-v0.zip

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tqc-donkey-warren-track-v0/_stable_baselines3_version

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+1.5.1a9

tqc-donkey-warren-track-v0/actor.optimizer.pth

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tqc-donkey-warren-track-v0/critic.optimizer.pth

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tqc-donkey-warren-track-v0/data

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+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVKgAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMCVRRQ1BvbGljeZSTlC4=",
+        "__module__": "sb3_contrib.tqc.policies",
+        "__doc__": "\n    Policy class (with both actor and critic) for TQC.\n\n    :param observation_space: Observation space\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 use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the feature extractor.\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    :param n_quantiles: Number of quantiles for the critic.\n    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
+        "__init__": "<function TQCPolicy.__init__ at 0x7fa38ae877a0>",
+        "_build": "<function TQCPolicy._build at 0x7fa38ae87830>",
+        "_get_constructor_parameters": "<function TQCPolicy._get_constructor_parameters at 0x7fa38ae878c0>",
+        "reset_noise": "<function TQCPolicy.reset_noise at 0x7fa38ae87950>",
+        "make_actor": "<function TQCPolicy.make_actor at 0x7fa38ae879e0>",
+        "make_critic": "<function TQCPolicy.make_critic at 0x7fa38ae87a70>",
+        "forward": "<function TQCPolicy.forward at 0x7fa38ae87b00>",
+        "_predict": "<function TQCPolicy._predict at 0x7fa38ae87b90>",
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+    },
+    "verbose": 1,
+    "policy_kwargs": {
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+        "net_arch": [
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+            256
+        ],
+        "n_critics": 2,
+        "use_expln": true,
+        "use_sde": true
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