Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: AcrobotSwingupDMC-v0
     metrics:
     - type: mean_reward
-      value: 96.89 +/- 64.09
+      value: 63.99 +/- 45.62
       name: mean_reward
       verified: false
 ---

args.yml

@@ -54,13 +54,13 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 3785955249
+    - 1984220215
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - runs/AcrobotSwingupDMC-v0__ddpg__3785955249__1673810878
+    - runs/AcrobotSwingupDMC-v0__ddpg__1984220215__1673811006
   - - track
     - true
   - - trained_agent

ddpg-AcrobotSwingupDMC-v0.zip

@@ -1,3 +1,3 @@
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ddpg-AcrobotSwingupDMC-v0/actor.optimizer.pth

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ddpg-AcrobotSwingupDMC-v0/critic.optimizer.pth

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 version https://git-lfs.github.com/spec/v1
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ddpg-AcrobotSwingupDMC-v0/data

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         "__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 0x135f98280>",
-        "_build": "<function TD3Policy._build at 0x135f98310>",
-        "_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x135f983a0>",
-        "make_actor": "<function TD3Policy.make_actor at 0x135f98430>",
-        "make_critic": "<function TD3Policy.make_critic at 0x135f984c0>",
-        "forward": "<function TD3Policy.forward at 0x135f98550>",
-        "_predict": "<function TD3Policy._predict at 0x135f985e0>",
-        "set_training_mode": "<function TD3Policy.set_training_mode at 0x135f98670>",
+        "__init__": "<function TD3Policy.__init__ at 0x15dd98280>",
+        "_build": "<function TD3Policy._build at 0x15dd98310>",
+        "_get_constructor_parameters": "<function TD3Policy._get_constructor_parameters at 0x15dd983a0>",
+        "make_actor": "<function TD3Policy.make_actor at 0x15dd98430>",
+        "make_critic": "<function TD3Policy.make_critic at 0x15dd984c0>",
+        "forward": "<function TD3Policy.forward at 0x15dd98550>",
+        "_predict": "<function TD3Policy._predict at 0x15dd985e0>",
+        "set_training_mode": "<function TD3Policy.set_training_mode at 0x15dd98670>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x135f91fc0>"
+        "_abc_impl": "<_abc._abc_data object at 0x15dd92ac0>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -31,7 +31,7 @@
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         "noise_prev": "[0.]"
     },
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     "learning_rate": {
         ":type:": "<class 'function'>",
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     },
-    "tensorboard_log": "runs/AcrobotSwingupDMC-v0__ddpg__3785955249__1673810878/AcrobotSwingupDMC-v0",
+    "tensorboard_log": "runs/AcrobotSwingupDMC-v0__ddpg__4000244894__1673811006/AcrobotSwingupDMC-v0",
     "lr_schedule": {
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@@ -87,7 +87,7 @@
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     "_last_original_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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+        ":serialized:": "gAWVjQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYYAAAAAAAAAGSnUj825ga90XcRv3Pcfz8rYwtAF5AWQZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsGhpSMAUOUdJRSlC4="
     },
     "_episode_num": 1000,
     "use_sde": false,
@@ -95,7 +95,7 @@
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     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -114,12 +114,12 @@
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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    ",
-        "__init__": "<function ReplayBuffer.__init__ at 0x135f96dd0>",
-        "add": "<function ReplayBuffer.add at 0x135f96e60>",
-        "sample": "<function ReplayBuffer.sample at 0x135f96ef0>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x135f96f80>",
+        "__init__": "<function ReplayBuffer.__init__ at 0x15dd96dd0>",
+        "add": "<function ReplayBuffer.add at 0x15dd96e60>",
+        "sample": "<function ReplayBuffer.sample at 0x15dd96ef0>",
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         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x135f3d540>"
+        "_abc_impl": "<_abc._abc_data object at 0x15dd36300>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {

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replay.mp4

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results.json

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