Initial Commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: -183.06 +/- 103.48
+      value: -176.33 +/- 101.55
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -50,13 +50,8 @@ python -m utils.push_to_hub --algo sac --env Pendulum-v1 -f logs/ -orga sb3
 
 ## Hyperparameters
 ```python
-OrderedDict([('gradient_steps', -1),
-             ('learning_rate', 0.001),
-             ('n_episodes_rollout', 1),
+OrderedDict([('learning_rate', 0.001),
              ('n_timesteps', 20000),
              ('policy', 'MlpPolicy'),
-             ('policy_kwargs', 'dict(log_std_init=-2, net_arch=[64, 64])'),
-             ('train_freq', -1),
-             ('use_sde', True),
              ('normalize', False)])
 ```

args.yml

@@ -1,24 +1,28 @@
 !!python/object/apply:collections.OrderedDict
 - - - algo
     - sac
+  - - device
+    - auto
   - - env
-    - Pendulum-v0
+    - Pendulum-v1
   - - env_kwargs
     - null
   - - eval_episodes
     - 5
   - - eval_freq
-    - 10000
+    - 25000
   - - gym_packages
     - []
   - - hyperparams
     - null
   - - log_folder
-    - rl-trained-agents/
+    - logs/
   - - log_interval
     - -1
+  - - n_eval_envs
+    - 1
   - - n_evaluations
-    - 20
+    - null
   - - n_jobs
     - 1
   - - n_startup_trials
@@ -26,9 +30,13 @@
   - - n_timesteps
     - -1
   - - n_trials
-    - 10
+    - 500
+  - - no_optim_plots
+    - false
   - - num_threads
-    - 2
+    - -1
+  - - optimization_log_path
+    - null
   - - optimize_hyperparameters
     - false
   - - pruner
@@ -40,16 +48,26 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 1228490524
+    - 561540835
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
     - ''
+  - - track
+    - false
   - - trained_agent
     - ''
+  - - truncate_last_trajectory
+    - true
   - - uuid
     - false
+  - - vec_env
+    - dummy
   - - verbose
     - 1
+  - - wandb_entity
+    - null
+  - - wandb_project_name
+    - sb3

config.yml

@@ -1,17 +1,7 @@
 !!python/object/apply:collections.OrderedDict
-- - - gradient_steps
-    - -1
-  - - learning_rate
+- - - learning_rate
     - 0.001
-  - - n_episodes_rollout
-    - 1
   - - n_timesteps
     - 20000
   - - policy
     - MlpPolicy
-  - - policy_kwargs
-    - dict(log_std_init=-2, net_arch=[64, 64])
-  - - train_freq
-    - -1
-  - - use_sde
-    - true

replay.mp4

@@ -1,3 +1,3 @@
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-size 163499
+oid sha256:9459b5b8d9aef043e3253027a511d82e40803247f38c1033492925045b67a966
+size 195933

results.json

@@ -1 +1 @@
-{"mean_reward": -183.05522760000002, "std_reward": 103.48458808056235, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-22T21:55:31.844628"}
+{"mean_reward": -176.32888, "std_reward": 101.54794864862134, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-22T22:23:07.885400"}

sac-Pendulum-v1.zip

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+size 3005853

sac-Pendulum-v1/actor.optimizer.pth

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-size 40059
+oid sha256:4cddb9ae9fe6a55c343cc452511559ee5c87394a3febfa996ae5234cd6d4650b
+size 542837

sac-Pendulum-v1/critic.optimizer.pth

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+size 1083485

sac-Pendulum-v1/data

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         "__module__": "stable_baselines3.sac.policies",
         "__doc__": "\n    Policy class (with both actor and critic) for SAC.\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 features 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_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    ",
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-        "_predict": "<function SACPolicy._predict at 0x7f1c154d9ef0>",
-        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f1c154d9f80>",
+        "__init__": "<function SACPolicy.__init__ at 0x7f5e9b6a1b00>",
+        "_build": "<function SACPolicy._build at 0x7f5e9b6a1b90>",
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+        "make_critic": "<function SACPolicy.make_critic at 0x7f5e9b6a1dd0>",
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         "__abstractmethods__": "frozenset()",
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     "verbose": 1,
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-        "net_arch": [
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-            64
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-        "use_sde": true
+        "use_sde": false
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         "dtype": "float32",
+        "_shape": [
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+        ],
         "low": "[-2.]",
         "high": "[2.]",
         "bounded_below": "[ True]",
         "bounded_above": "[ True]",
-        "_shape": [
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-        ],
         "_np_random": "RandomState(MT19937)"
     },
     "n_envs": 1,
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     "seed": 0,
     "action_noise": null,
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+    "start_time": 1653249455.1774566,
     "learning_rate": {
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     "tensorboard_log": null,
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