Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: FetchPickAndPlaceDense-v2
     metrics:
     - type: mean_reward
-      value: -12.37 +/- 7.16
+      value: -13.25 +/- 5.87
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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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 MultiInputPolicy.__init__ at 0x7887ac9d56c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7887ac9d8fc0>"}, "verbose": 1, "policy_kwargs": {"net_arch": [512, 512, 512]}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1719190071682219297, "learning_rate": 0.0003723786142741777, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": 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         "__module__": "stable_baselines3.td3.policies",
         "__doc__": "\n    Policy class (with both actor and critic) for TD3 to be used with Dict observation spaces.\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    ",
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+        "__init__": "<function MultiInputPolicy.__init__ at 0x781f2d9b1990>",
         "__abstractmethods__": "frozenset()",
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+        "_abc_impl": "<_abc._abc_data object at 0x781f2d7bc740>"
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+    "verbose": 0,
     "policy_kwargs": {
         "net_arch": [
             512,
@@ -16,46 +16,25 @@
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         "bounded_below": "[ True  True  True  True]",
         "bounded_above": "[ True  True  True  True]",
@@ -77,14 +56,14 @@
         "high": "[1. 1. 1. 1.]",
         "low_repr": "-1.0",
         "high_repr": "1.0",
-        "_np_random": "Generator(PCG64)"
+        "_np_random": null
     },
     "n_envs": 1,
     "buffer_size": 1000000,
-    "batch_size": 64,
+    "batch_size": 100,
     "learning_starts": 100,
     "tau": 0.005,
-    "gamma": 0.9512747873959645,
+    "gamma": 0.99,
     "gradient_steps": -1,
     "optimize_memory_usage": false,
     "replay_buffer_class": {
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         "__module__": "stable_baselines3.common.buffers",
         "__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    ",
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-        "add": "<function DictReplayBuffer.add at 0x7887ac9d45e0>",
-        "sample": "<function DictReplayBuffer.sample at 0x7887ac9d4670>",
-        "_get_samples": "<function DictReplayBuffer._get_samples at 0x7887ac9d4700>",
+        "__init__": "<function DictReplayBuffer.__init__ at 0x781f2d9b0820>",
+        "add": "<function DictReplayBuffer.add at 0x781f2d9b08b0>",
+        "sample": "<function DictReplayBuffer.sample at 0x781f2d9b0940>",
+        "_get_samples": "<function DictReplayBuffer._get_samples at 0x781f2d9b09d0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7887ac9c3400>"
+        "_abc_impl": "<_abc._abc_data object at 0x781f2d9a6b40>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {
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     "target_policy_noise": 0.2,
     "lr_schedule": {
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     "actor_batch_norm_stats": [],
     "critic_batch_norm_stats": [],

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