Update to new sim

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 3000.47 +/- 12.58
+      value: 363.88 +/- 0.94
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -35,12 +35,6 @@ 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
 
-Autoencoder: https://github.com/araffin/aae-train-donkeycar branch: `feat/race_june` <br/>
-Gym env: https://github.com/araffin/gym-donkeycar-1 branch: `feat/race_june` <br/>
-RL Zoo branch: `feat/gym-donkeycar`
-
-**Pretrained autoencoder** can be downloaded here: https://github.com/araffin/aae-train-donkeycar/releases/download/live-twitch-2/ae-32_mountain.pkl
-
 ```
 # Download model and save it into the logs/ folder
 python -m utils.load_from_hub --algo tqc --env donkey-mountain-track-v0 -orga araffin -f logs/
@@ -63,7 +57,8 @@ OrderedDict([('batch_size', 256),
                'utils.callbacks.LapTimeCallback']),
              ('ent_coef', 'auto'),
              ('env_wrapper',
-              ['ae.wrapper.AutoencoderWrapper',
+              [{'gym.wrappers.time_limit.TimeLimit': {'max_episode_steps': 10000}},
+               'ae.wrapper.AutoencoderWrapper',
                {'utils.wrappers.HistoryWrapper': {'horizon': 2}}]),
              ('gamma', 0.99),
              ('gradient_steps', 256),
@@ -97,5 +92,6 @@ OrderedDict([('batch_size', 256),
           'max_cte': 16,
           'port': 9091,
           'start_delay': 5.0},
- 'steer': 0.5}
+ 'min_throttle': -0.2,
+ 'steer': 0.3}
 ```

args.yml

@@ -11,6 +11,14 @@
         - 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: mountain_track
@@ -18,7 +26,8 @@
         max_cte: 16
         port: 9091
         start_delay: 5.0
-      steer: 0.5
+      min_throttle: -0.2
+      steer: 0.3
   - - eval_episodes
     - 5
   - - eval_freq
@@ -26,11 +35,13 @@
   - - gym_packages
     - []
   - - hyperparams
-    - learning_starts: 500
+    - null
   - - log_folder
     - logs
   - - log_interval
     - -1
+  - - max_total_trials
+    - null
   - - n_eval_envs
     - 1
   - - n_evaluations
@@ -47,16 +58,10 @@
     - false
   - - num_threads
     - -1
-  - - offline_algo
-    - null
   - - optimization_log_path
     - null
   - - optimize_hyperparameters
     - false
-  - - pretrain_buffer
-    - null
-  - - pretrain_params
-    - null
   - - pruner
     - median
   - - sampler
@@ -66,13 +71,13 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 4190083556
+    - 1165537380
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - runs/donkey-mountain-track-v0__tqc__4190083556__1649751466
+    - runs/donkey-mountain-track-v0__tqc__1165537380__1654422330
   - - track
     - true
   - - trained_agent
@@ -86,6 +91,6 @@
   - - verbose
     - 1
   - - wandb_entity
-    - null
+    - sb3
   - - wandb_project_name
     - donkeycar

config.yml

@@ -10,7 +10,9 @@
   - - ent_coef
     - auto
   - - env_wrapper
-    - - ae.wrapper.AutoencoderWrapper
+    - - gym.wrappers.time_limit.TimeLimit:
+          max_episode_steps: 10000
+      - ae.wrapper.AutoencoderWrapper
       - utils.wrappers.HistoryWrapper:
           horizon: 2
   - - gamma

env_kwargs.yml

@@ -18,4 +18,5 @@ conf:
   max_cte: 16
   port: 9091
   start_delay: 5.0
-steer: 0.5
+min_throttle: -0.2
+steer: 0.3

replay.mp4

@@ -1,3 +1,3 @@
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-size 1532937
+oid sha256:08509c12d98dfd425ebb11fb4f4300e6e39370c9c233dad5cb13b63680ea1cf7
+size 1499368

results.json

@@ -1 +1 @@
-{"mean_reward": 3000.4744345, "std_reward": 12.58479606855654, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-04T22:31:11.115167"}
+{"mean_reward": 363.88236049999995, "std_reward": 0.9449308918473567, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-05T17:44:07.220665"}

tqc-donkey-mountain-track-v0.zip

@@ -1,3 +1,3 @@
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-size 3968979
+oid sha256:c3cd61e783173a83c2c39af9849940df04d7aa97d383f3a31c5ece07f4eb61bc
+size 3969877

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

@@ -1,3 +1,3 @@
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-oid sha256:3023d37981db7d33b53a63081cf37a25c33f60efd1b644ab4b70f49f54ec1673
-size 683707
+oid sha256:32bd20c46c5e07ea560170aaafccad95ba5d30955469211b5f0037bebf5511d2
+size 683835

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

@@ -1,3 +1,3 @@
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-oid sha256:d77d8dd41f1aade31e7c9ca09df5f191d2327884906c29b7074ac8f92a9372a2
-size 1460637
+oid sha256:36d7bb586fe077766ba6747b5a6f4e2c07f7a4e7d95760743bffcbfd7e05e5ea
+size 1460893

tqc-donkey-mountain-track-v0/data

@@ -4,17 +4,17 @@
         ":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 0x7f50d6d963b0>",
-        "_build": "<function TQCPolicy._build at 0x7f50d6d96440>",
-        "_get_constructor_parameters": "<function TQCPolicy._get_constructor_parameters at 0x7f50d6d964d0>",
-        "reset_noise": "<function TQCPolicy.reset_noise at 0x7f50d6d96560>",
-        "make_actor": "<function TQCPolicy.make_actor at 0x7f50d6d965f0>",
-        "make_critic": "<function TQCPolicy.make_critic at 0x7f50d6d96680>",
-        "forward": "<function TQCPolicy.forward at 0x7f50d6d96710>",
-        "_predict": "<function TQCPolicy._predict at 0x7f50d6d967a0>",
-        "set_training_mode": "<function TQCPolicy.set_training_mode at 0x7f50d6d96830>",
+        "__init__": "<function TQCPolicy.__init__ at 0x7f4d85780560>",
+        "_build": "<function TQCPolicy._build at 0x7f4d857805f0>",
+        "_get_constructor_parameters": "<function TQCPolicy._get_constructor_parameters at 0x7f4d85780680>",
+        "reset_noise": "<function TQCPolicy.reset_noise at 0x7f4d85780710>",
+        "make_actor": "<function TQCPolicy.make_actor at 0x7f4d857807a0>",
+        "make_critic": "<function TQCPolicy.make_critic at 0x7f4d85780830>",
+        "forward": "<function TQCPolicy.forward at 0x7f4d857808c0>",
+        "_predict": "<function TQCPolicy._predict at 0x7f4d85780950>",
+        "set_training_mode": "<function TQCPolicy.set_training_mode at 0x7f4d857809e0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f50d6d83810>"
+        "_abc_impl": "<_abc_data object at 0x7f4d8577d1e0>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -29,26 +29,26 @@
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     "observation_space": {
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         "dtype": "float32",
         "_shape": [
             2
         ],
-        "low": "[-0.5  0. ]",
-        "high": "[0.5 1. ]",
+        "low": "[-0.3 -0.2]",
+        "high": "[0.3 1. ]",
         "bounded_below": "[ True  True]",
         "bounded_above": "[ True  True]",
         "_np_random": "RandomState(MT19937)"
@@ -59,12 +59,12 @@
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
-    "start_time": 1649751476.6629932,
+    "start_time": 1654422341.1686037,
     "learning_rate": {
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     },
-    "tensorboard_log": "runs/donkey-mountain-track-v0__tqc__4190083556__1649751466/donkey-mountain-track-v0",
+    "tensorboard_log": "runs/donkey-mountain-track-v0__tqc__1165537380__1654422330/donkey-mountain-track-v0",
     "lr_schedule": {
         ":type:": "<class 'function'>",
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@@ -76,7 +76,7 @@
     },
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