Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: CartpoleDMC-v0
     metrics:
     - type: mean_reward
-      value: 991.74 +/- 0.45
+      value: 885.50 +/- 4.94
       name: mean_reward
       verified: false
 ---
@@ -62,7 +62,12 @@ python -m rl_zoo3.push_to_hub --algo ddpg --env CartpoleDMC-v0 -f logs/ -orga qg
 
 ## Hyperparameters
 ```python
-OrderedDict([('n_timesteps', 1000000.0),
+OrderedDict([('batch_size', 64),
+             ('gamma', 0.99),
+             ('learning_rate', 0.0001),
+             ('n_timesteps', 1000000.0),
+             ('noise_std', 0.3),
+             ('noise_type', 'ornstein-uhlenbeck'),
              ('policy', 'MlpPolicy'),
              ('policy_kwargs',
               'dict(net_arch=dict(pi=[300, 200], qf=[400, 300]))'),

args.yml

@@ -10,11 +10,11 @@
   - - env_kwargs
     - null
   - - eval_episodes
-    - 5
+    - 20
   - - eval_freq
     - 25000
   - - gym_packages
-    - - dmc_gym
+    - []
   - - hyperparams
     - null
   - - log_folder
@@ -24,7 +24,7 @@
   - - max_total_trials
     - null
   - - n_eval_envs
-    - 1
+    - 5
   - - n_evaluations
     - null
   - - n_jobs
@@ -54,15 +54,15 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 1840051874
+    - 3757117300
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - ''
+    - runs/CartpoleDMC-v0__ddpg__3757117300__1673811016
   - - track
-    - false
+    - true
   - - trained_agent
     - ''
   - - truncate_last_trajectory
@@ -74,9 +74,9 @@
   - - verbose
     - 1
   - - wandb_entity
-    - null
+    - qgallouedec
   - - wandb_project_name
-    - sb3
+    - dmc
   - - wandb_tags
     - []
   - - yaml_file

config.yml

@@ -1,6 +1,16 @@
 !!python/object/apply:collections.OrderedDict
-- - - n_timesteps
+- - - batch_size
+    - 64
+  - - gamma
+    - 0.99
+  - - learning_rate
+    - 0.0001
+  - - n_timesteps
     - 1000000.0
+  - - noise_std
+    - 0.3
+  - - noise_type
+    - ornstein-uhlenbeck
   - - policy
     - MlpPolicy
   - - policy_kwargs

ddpg-CartpoleDMC-v0.zip

@@ -1,3 +1,3 @@
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-size 3010029
+oid sha256:877cd406d523ea85172a4a93ff71c81e540dec32903d4bc68f23d8a1f8873e9a
+size 3011980

ddpg-CartpoleDMC-v0/actor.optimizer.pth

@@ -1,3 +1,3 @@
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-oid sha256:cb20b1fe7bab4e0da2512800c8914f14964df922fc5e0eac9e6ab98deefea832
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 size 502319

ddpg-CartpoleDMC-v0/critic.optimizer.pth

@@ -1,3 +1,3 @@
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-oid sha256:d120ee30a9e4a1c5c8d20f75573f83f7ad0261a31f55c9bb2ca16f22df0c6441
+oid sha256:b00f7c9cecf7772b2df007c1577f2f50d5b7e298708baed00becbb269abe6a3f
 size 991855

ddpg-CartpoleDMC-v0/data

@@ -4,16 +4,16 @@
         ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnRkMy5wb2xpY2llc5SMCVREM1BvbGljeZSTlC4=",
         "__module__": "stable_baselines3.td3.policies",
         "__doc__": "\n    Policy class (with both actor and critic) for TD3.\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 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    ",
-        "__init__": "<function TD3Policy.__init__ at 0x153e1c700>",
-        "_build": "<function TD3Policy._build at 0x153e1c790>",
-        "_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x153e1c820>",
-        "make_actor": "<function TD3Policy.make_actor at 0x153e1c8b0>",
-        "make_critic": "<function TD3Policy.make_critic at 0x153e1c940>",
-        "forward": "<function TD3Policy.forward at 0x153e1c9d0>",
-        "_predict": "<function TD3Policy._predict at 0x153e1ca60>",
-        "set_training_mode": "<function TD3Policy.set_training_mode at 0x153e1caf0>",
+        "__init__": "<function TD3Policy.__init__ at 0x139898280>",
+        "_build": "<function TD3Policy._build at 0x139898310>",
+        "_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x1398983a0>",
+        "make_actor": "<function TD3Policy.make_actor at 0x139898430>",
+        "make_critic": "<function TD3Policy.make_critic at 0x1398984c0>",
+        "forward": "<function TD3Policy.forward at 0x139898550>",
+        "_predict": "<function TD3Policy._predict at 0x1398985e0>",
+        "set_training_mode": "<function TD3Policy.set_training_mode at 0x139898670>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x153e15e80>"
+        "_abc_impl": "<_abc._abc_data object at 0x139892d80>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -31,7 +31,7 @@
     },
     "observation_space": {
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     "_last_obs": null,
     "_last_episode_starts": {
@@ -75,7 +87,7 @@
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@@ -83,7 +95,7 @@
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     "_n_updates": 1000000,
     "buffer_size": 1,
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     "learning_starts": 100,
     "tau": 0.005,
     "gamma": 0.99,
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         "__module__": "stable_baselines3.common.buffers",
         "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\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    ",
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-        "sample": "<function ReplayBuffer.sample at 0x153e0b370>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x153e0b400>",
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+        "sample": "<function ReplayBuffer.sample at 0x139896ef0>",
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         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x153e03f40>"
+        "_abc_impl": "<_abc._abc_data object at 0x139836280>"
     },
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replay.mp4

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results.json

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