model

README.md

@@ -16,7 +16,7 @@ model-index:
       type: Ant-v5
     metrics:
     - type: mean_reward
-      value: 6061.68 +/- 1036.52
+      value: 6022.47 +/- 1093.46
       name: mean_reward
       verified: false
 ---

ant-v5-SAC-medium.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7816f1f25fb0ab5f44da9c603e85f77b871a0b28d4bc900e925ea8e9fd11046b
+oid sha256:dea0e6dbb847776ac9e91dc8b7df5ba975742924eb55831d2da08a0abc786ca2
 size 4275932

ant-v5-SAC-medium/data

@@ -5,17 +5,17 @@
         "__module__": "stable_baselines3.sac.policies",
         "__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}",
         "__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 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    ",
-        "__init__": "<function SACPolicy.__init__ at 0x7f262f614e00>",
-        "_build": "<function SACPolicy._build at 0x7f262f6153a0>",
-        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f262f615440>",
-        "reset_noise": "<function SACPolicy.reset_noise at 0x7f262f6154e0>",
-        "make_actor": "<function SACPolicy.make_actor at 0x7f262f615580>",
-        "make_critic": "<function SACPolicy.make_critic at 0x7f262f615620>",
-        "forward": "<function SACPolicy.forward at 0x7f262f6156c0>",
-        "_predict": "<function SACPolicy._predict at 0x7f262f615760>",
-        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f262f615800>",
+        "__init__": "<function SACPolicy.__init__ at 0x7f3a967f8e00>",
+        "_build": "<function SACPolicy._build at 0x7f3a967f93a0>",
+        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f3a967f9440>",
+        "reset_noise": "<function SACPolicy.reset_noise at 0x7f3a967f94e0>",
+        "make_actor": "<function SACPolicy.make_actor at 0x7f3a967f9580>",
+        "make_critic": "<function SACPolicy.make_critic at 0x7f3a967f9620>",
+        "forward": "<function SACPolicy.forward at 0x7f3a967f96c0>",
+        "_predict": "<function SACPolicy._predict at 0x7f3a967f9760>",
+        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f3a967f9800>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f262f60ffc0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f3a967fd580>"
     },
     "verbose": 0,
     "policy_kwargs": {
@@ -68,13 +68,13 @@
         "__module__": "stable_baselines3.common.buffers",
         "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'next_observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'dones': <class 'numpy.ndarray'>, 'timeouts': <class 'numpy.ndarray'>}",
         "__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 0x7f263a450d60>",
-        "add": "<function ReplayBuffer.add at 0x7f263a450ea0>",
-        "sample": "<function ReplayBuffer.sample at 0x7f263a450f40>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x7f263a450fe0>",
-        "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7f263a451080>)>",
+        "__init__": "<function ReplayBuffer.__init__ at 0x7f3aa15d0d60>",
+        "add": "<function ReplayBuffer.add at 0x7f3aa15d0ea0>",
+        "sample": "<function ReplayBuffer.sample at 0x7f3aa15d0f40>",
+        "_get_samples": "<function ReplayBuffer._get_samples at 0x7f3aa15d0fe0>",
+        "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7f3aa15d1080>)>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f263a449940>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f3aa1dc6c80>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMCVNBQ1BvbGljeZSTlC4=", "__module__": "stable_baselines3.sac.policies", "__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}", "__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 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    ", "__init__": "<function SACPolicy.__init__ at 0x7f262f614e00>", "_build": "<function SACPolicy._build at 0x7f262f6153a0>", "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f262f615440>", "reset_noise": "<function SACPolicy.reset_noise at 0x7f262f6154e0>", "make_actor": "<function SACPolicy.make_actor at 0x7f262f615580>", "make_critic": "<function SACPolicy.make_critic at 0x7f262f615620>", "forward": "<function SACPolicy.forward at 0x7f262f6156c0>", "_predict": "<function SACPolicy._predict at 0x7f262f615760>", "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f262f615800>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f262f60ffc0>"}, "verbose": 0, "policy_kwargs": {"use_sde": false}, "num_timesteps": 8770000, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": 0, "action_noise": null, "start_time": 1737252144310799463, "learning_rate": 0.0003, "tensorboard_log": "runs/0", "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": 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results.json

@@ -1 +1 @@
-{"mean_reward": 6061.678638562, "std_reward": 1036.5249535883697, "is_deterministic": true, "n_eval_episodes": 1000, "eval_datetime": "2025-01-26T15:35:53.330786"}
+{"mean_reward": 6022.472008907, "std_reward": 1093.4592245178594, "is_deterministic": true, "n_eval_episodes": 1000, "eval_datetime": "2025-01-26T16:17:34.086941"}