Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: ReachCube-v0
     metrics:
     - type: mean_reward
-      value: -88.83 +/- 25.22
+      value: -59.79 +/- 11.56
       name: mean_reward
       verified: false
 ---

args.yml

@@ -56,7 +56,7 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 1136752841
+    - 2637980760
   - - storage
     - null
   - - study_name

results.json

@@ -1 +1 @@
-{"mean_reward": -88.82967179999999, "std_reward": 25.21824856666716, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-13T14:19:17.697076"}
+{"mean_reward": -59.785499800000004, "std_reward": 11.556857543993244, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-13T15:15:15.088761"}

tqc-ReachCube-v0.zip

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tqc-ReachCube-v0/actor.optimizer.pth

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tqc-ReachCube-v0/critic.optimizer.pth

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tqc-ReachCube-v0/data

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         ":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu",
         "__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 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 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 MultiInputPolicy.__init__ at 0x7fb40930f7f0>",
+        "__init__": "<function MultiInputPolicy.__init__ at 0x7f9ebb4677f0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fb409313dc0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f9ebb47c2c0>"
     },
     "verbose": 1,
     "policy_kwargs": {
         "use_sde": true
     },
-    "num_timesteps": 274912,
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     "_total_timesteps": 10000000,
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
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+    "start_time": 1718291358584308985,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
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         "__annotations__": "{'observation_space': <class 'gymnasium.spaces.dict.Dict'>, 'obs_shape': typing.Dict[str, typing.Tuple[int, ...]], 'observations': typing.Dict[str, numpy.ndarray], 'next_observations': typing.Dict[str, numpy.ndarray]}",
         "__doc__": "\n    Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n    Extends the ReplayBuffer to use dictionary observations\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        Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\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 DictReplayBuffer.__init__ at 0x7fb409cc23b0>",
-        "add": "<function DictReplayBuffer.add at 0x7fb409cc2440>",
-        "sample": "<function DictReplayBuffer.sample at 0x7fb409cc24d0>",
-        "_get_samples": "<function DictReplayBuffer._get_samples at 0x7fb409cc2560>",
+        "__init__": "<function DictReplayBuffer.__init__ at 0x7f9ebbe1e3b0>",
+        "add": "<function DictReplayBuffer.add at 0x7f9ebbe1e440>",
+        "sample": "<function DictReplayBuffer.sample at 0x7f9ebbe1e4d0>",
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         "__abstractmethods__": "frozenset()",
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+        "_abc_impl": "<_abc._abc_data object at 0x7f9ebbe18f00>"
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     "replay_buffer_kwargs": {},
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