model

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.gitattributes +1 -0
README.md +1 -1
ant-v5-sac-medium.zip +2 -2
ant-v5-sac-medium/actor.optimizer.pth +1 -1
ant-v5-sac-medium/critic.optimizer.pth +1 -1
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ant-v5-sac-medium/ent_coef_optimizer.pth +1 -1
ant-v5-sac-medium/policy.pth +1 -1
ant-v5-sac-medium/pytorch_variables.pth +1 -1
config.json +1 -1
replay.mp4 +0 -0
results.json +1 -1

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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md CHANGED Viewed

@@ -16,7 +16,7 @@ model-index:
       type: Ant-v5
     metrics:
     - type: mean_reward
-      value: 571.34 +/- 332.17
       name: mean_reward
       verified: false
 ---

       type: Ant-v5
     metrics:
     - type: mean_reward
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       name: mean_reward
       verified: false
 ---

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         "__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 0x7f3ba5a62f80>",
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-        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f3ba5a630a0>",
-        "reset_noise": "<function SACPolicy.reset_noise at 0x7f3ba5a63130>",
-        "make_actor": "<function SACPolicy.make_actor at 0x7f3ba5a631c0>",
-        "make_critic": "<function SACPolicy.make_critic at 0x7f3ba5a63250>",
-        "forward": "<function SACPolicy.forward at 0x7f3ba5a632e0>",
-        "_predict": "<function SACPolicy._predict at 0x7f3ba5a63370>",
-        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f3ba5a63400>",
         "__abstractmethods__": "frozenset()",
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         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 37999,
     "buffer_size": 1000000,
     "batch_size": 256,
     "learning_starts": 10000,
@@ -67,13 +67,13 @@
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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 0x7f3ba5a51cf0>",
-        "add": "<function ReplayBuffer.add at 0x7f3ba5a51d80>",
-        "sample": "<function ReplayBuffer.sample at 0x7f3ba5a51e10>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x7f3ba5a51ea0>",
-        "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7f3ba5a51f30>)>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f3ba5bbab40>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {
@@ -101,7 +101,7 @@
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     "action_space": {
         ":type:": "<class 'gymnasium.spaces.box.Box'>",
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         "dtype": "float32",
         "bounded_below": "[ True  True  True  True  True  True  True  True]",
         "bounded_above": "[ True  True  True  True  True  True  True  True]",

         "__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 0x7f6b5fd62f80>",
+        "_build": "<function SACPolicy._build at 0x7f6b5fd63010>",
+        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f6b5fd630a0>",
+        "reset_noise": "<function SACPolicy.reset_noise at 0x7f6b5fd63130>",
+        "make_actor": "<function SACPolicy.make_actor at 0x7f6b5fd631c0>",
+        "make_critic": "<function SACPolicy.make_critic at 0x7f6b5fd63250>",
+        "forward": "<function SACPolicy.forward at 0x7f6b5fd632e0>",
+        "_predict": "<function SACPolicy._predict at 0x7f6b5fd63370>",
+        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f6b5fd63400>",
         "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f6b5fd66ac0>"
     },
     "verbose": 0,
     "policy_kwargs": {
         "use_sde": false
     },
+    "num_timesteps": 3000000,
     "_total_timesteps": 3000000,
     "_num_timesteps_at_start": 0,
+    "seed": 0,
     "action_noise": null,
+    "start_time": 1699235878180773774,
     "learning_rate": 0.0003,
+    "tensorboard_log": "runs/7mlhu3nu",
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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     "_last_episode_starts": {
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     },
     "_last_original_obs": {
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It allows to keep variance\n above zero and prevent it from growing too fast. 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It allows to keep variance\n above zero and prevent it from growing too fast. 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Binary files a/replay.mp4 and b/replay.mp4 differ

results.json CHANGED Viewed

	@@ -1 +1 @@
1	- {"mean_reward": ~~571~~.~~3430997~~, "std_reward": ~~332~~.~~16894945693326~~, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-06T19:24:15.~~854590~~"}


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