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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
ant-v5-sac-medium/data +25 -25
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 +2 -2
results.json +1 -1

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ model-index:
       type: Ant-v5
     metrics:
     - type: mean_reward
-      value: 1677.84 +/- 848.55
       name: mean_reward
       verified: false
 ---

       type: Ant-v5
     metrics:
     - type: mean_reward
+      value: 4473.16 +/- 1392.04
       name: mean_reward
       verified: false
 ---

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ant-v5-sac-medium/data CHANGED Viewed

@@ -5,33 +5,33 @@
         "__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 0x7fdd5b9584a0>",
-        "_build": "<function SACPolicy._build at 0x7fdd5b958ae0>",
-        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7fdd5b958b80>",
-        "reset_noise": "<function SACPolicy.reset_noise at 0x7fdd5b958c20>",
-        "make_actor": "<function SACPolicy.make_actor at 0x7fdd5b958cc0>",
-        "make_critic": "<function SACPolicy.make_critic at 0x7fdd5b958d60>",
-        "forward": "<function SACPolicy.forward at 0x7fdd5b958e00>",
-        "_predict": "<function SACPolicy._predict at 0x7fdd5b958ea0>",
-        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7fdd5b958f40>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fdd5b95d540>"
     },
     "verbose": 0,
     "policy_kwargs": {
         "use_sde": false
     },
-    "num_timesteps": 1000000,
-    "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
-    "start_time": 1730901255514565729,
     "learning_rate": 0.0003,
-    "tensorboard_log": "runs/r09bii4z",
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     "buffer_size": 1000000,
     "batch_size": 256,
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@@ -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 0x7fdd66760cc0>",
-        "add": "<function ReplayBuffer.add at 0x7fdd66760e00>",
-        "sample": "<function ReplayBuffer.sample at 0x7fdd66760ea0>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x7fdd66760f40>",
-        "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7fdd66760fe0>)>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7fdd66753180>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {

         "__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 0x7f64f0cf44a0>",
+        "_build": "<function SACPolicy._build at 0x7f64f0cf4ae0>",
+        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f64f0cf4b80>",
+        "reset_noise": "<function SACPolicy.reset_noise at 0x7f64f0cf4c20>",
+        "make_actor": "<function SACPolicy.make_actor at 0x7f64f0cf4cc0>",
+        "make_critic": "<function SACPolicy.make_critic at 0x7f64f0cf4d60>",
+        "forward": "<function SACPolicy.forward at 0x7f64f0cf4e00>",
+        "_predict": "<function SACPolicy._predict at 0x7f64f0cf4ea0>",
+        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f64f0cf4f40>",
         "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f64f0cf8a40>"
     },
     "verbose": 0,
     "policy_kwargs": {
         "use_sde": false
     },
+    "num_timesteps": 10000000,
+    "_total_timesteps": 10000000,
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
+    "start_time": 1730910441542966752,
     "learning_rate": 0.0003,
+    "tensorboard_log": "runs/p2aaafqn",
     "_last_obs": {
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     "_last_episode_starts": {
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     "use_sde": false,
     "sde_sample_freq": -1,
     "_current_progress_remaining": 0.0,
     "_stats_window_size": 100,
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