Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -22.40 +/- 7.73
+      value: -1.39 +/- 0.30
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3c89e13dccdefbddf22da0147d697d690dc0a4ef23aeb35c6ac84bac3797c86a
-size 103755
+oid sha256:553a5a27a31a1c271e43b83db846ef179db4400b0a0a3d17df5a3724ae8052c8
+size 108032

a2c-PandaReachDense-v2/data

@@ -4,9 +4,9 @@
         ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
         "__module__": "stable_baselines3.common.policies",
         "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ",
-        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7fa63c4d0a60>",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f10b8e3ba60>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fa63c4bfd50>"
+        "_abc_impl": "<_abc_data object at 0x7f10b8e34c90>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -19,12 +19,12 @@
             "weight_decay": 0
         }
     },
-    "num_timesteps": 1000,
-    "_total_timesteps": 1000,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1682494446377164262,
+    "start_time": 1682495000724598241,
     "learning_rate": 0.0007,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -33,10 +33,10 @@
     },
     "_last_obs": {
         ":type:": "<class 'collections.OrderedDict'>",
-        ":serialized:": "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",
-        "achieved_goal": "[[ 0.4662246  -0.30622184  0.9402406 ]\n [ 0.4662246  -0.30622184  0.9402406 ]\n [ 0.4662246  -0.30622184  0.9402406 ]\n [ 0.4662246  -0.30622184  0.9402406 ]]",
-        "desired_goal": "[[-0.5481118   1.357561   -0.94315   ]\n [-0.49097213 -0.79237723 -0.52946746]\n [ 0.38113752 -1.6158689   1.7662437 ]\n [-1.6462204  -1.6912585   1.6016108 ]]",
-        "observation": "[[ 0.4662246  -0.30622184  0.9402406   0.10796438 -0.05316231  0.07094373]\n [ 0.4662246  -0.30622184  0.9402406   0.10796438 -0.05316231  0.07094373]\n [ 0.4662246  -0.30622184  0.9402406   0.10796438 -0.05316231  0.07094373]\n [ 0.4662246  -0.30622184  0.9402406   0.10796438 -0.05316231  0.07094373]]"
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAuJzgPjOMYbzq/RA/uJzgPjOMYbzq/RA/uJzgPjOMYbzq/RA/uJzgPjOMYbzq/RA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAACnzTPn9h278ZL7q/lz5NP1VIXb82pJ2/Nb2IP/OrQ78P0Ag/Do6Kv8MzjLyVZz6/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAC4nOA+M4xhvOr9ED9kwR88kbxtu85eezu4nOA+M4xhvOr9ED9kwR88kbxtu85eezu4nOA+M4xhvOr9ED9kwR88kbxtu85eezu4nOA+M4xhvOr9ED9kwR88kbxtu85eezuUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[ 0.43869567 -0.01376634  0.5663744 ]\n [ 0.43869567 -0.01376634  0.5663744 ]\n [ 0.43869567 -0.01376634  0.5663744 ]\n [ 0.43869567 -0.01376634  0.5663744 ]]",
+        "desired_goal": "[[ 0.41305572 -1.7139128  -1.4545623 ]\n [ 0.8017363  -0.86438495 -1.2315738 ]\n [ 1.0682741  -0.7643425   0.5344247 ]\n [-1.0824602  -0.01711453 -0.74376804]]",
+        "observation": "[[ 0.43869567 -0.01376634  0.5663744   0.0097507  -0.00362757  0.00383561]\n [ 0.43869567 -0.01376634  0.5663744   0.0097507  -0.00362757  0.00383561]\n [ 0.43869567 -0.01376634  0.5663744   0.0097507  -0.00362757  0.00383561]\n [ 0.43869567 -0.01376634  0.5663744   0.0097507  -0.00362757  0.00383561]]"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -44,9 +44,9 @@
     },
     "_last_original_obs": {
         ":type:": "<class 'collections.OrderedDict'>",
-        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAyncDPZIwl704CGk+dCBwvIxvPj0MyGo+Fd2uPOk3ur0emy0+PUBtO10ar7s1KYI+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==",
+        ":serialized:": "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",
         "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
-        "desired_goal": "[[ 0.03209666 -0.07382311  0.22757041]\n [-0.01465617  0.0464931   0.22927874]\n [ 0.02134565 -0.09092695  0.16953704]\n [ 0.00362016 -0.00534372  0.25422063]]",
+        "desired_goal": "[[ 0.0937257   0.08934379  0.1315598 ]\n [ 0.08749653 -0.01008056  0.12238649]\n [-0.08935452 -0.09130254  0.2831508 ]\n [ 0.07884658  0.07835177  0.23704241]]",
         "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
     },
     "_episode_num": 0,
@@ -56,13 +56,13 @@
     "_stats_window_size": 100,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 50,
+    "_n_updates": 50000,
     "n_steps": 5,
     "gamma": 0.99,
     "gae_lambda": 1.0,

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8e43c31ab508d0ca651ecfc0191c07a1ce019a415d258280a4f572b82bc0da4f
+oid sha256:446b6a3d7bf48cceda21c485376abb9bce56ccf73c3a728225b6e8fde2509c80
 size 44670

a2c-PandaReachDense-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:941467ac2c044b6383b98e3971701a6be739b26c8c09cc75d0d1465ccb45ced4
+oid sha256:e4d1dd760e7d53231c00fc8ba1d53c02923905cae83da4a77daebff00b3dece9
 size 46014

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7fa63c4d0a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa63c4bfd50>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000, "_total_timesteps": 1000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682494446377164262, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.4662246  -0.30622184  0.9402406 ]\n [ 0.4662246  -0.30622184  0.9402406 ]\n [ 0.4662246  -0.30622184  0.9402406 ]\n [ 0.4662246  -0.30622184  0.9402406 ]]", "desired_goal": "[[-0.5481118   1.357561   -0.94315   ]\n [-0.49097213 -0.79237723 -0.52946746]\n [ 0.38113752 -1.6158689   1.7662437 ]\n [-1.6462204  -1.6912585   1.6016108 ]]", "observation": "[[ 0.4662246  -0.30622184  0.9402406   0.10796438 -0.05316231  0.07094373]\n [ 0.4662246  -0.30622184  0.9402406   0.10796438 -0.05316231  0.07094373]\n [ 0.4662246  -0.30622184  0.9402406   0.10796438 -0.05316231  0.07094373]\n [ 0.4662246  -0.30622184  0.9402406   0.10796438 -0.05316231  0.07094373]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[ 0.03209666 -0.07382311  0.22757041]\n [-0.01465617  0.0464931   0.22927874]\n [ 0.02134565 -0.09092695  0.16953704]\n [ 0.00362016 -0.00534372  0.25422063]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.15.0-52-generic-x86_64-with-glibc2.29 # 58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.8.0", "PyTorch": "1.12.1", "GPU Enabled": "True", "Numpy": "1.23.4", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f10b8e3ba60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f10b8e34c90>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682495000724598241, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWV1QIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMUS9ob21lL3VidW50dS8ubG9jYWwvbGliL3B5dGhvbjMuOC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMUS9ob21lL3VidW50dS8ubG9jYWwvbGliL3B5dGhvbjMuOC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.43869567 -0.01376634  0.5663744 ]\n [ 0.43869567 -0.01376634  0.5663744 ]\n [ 0.43869567 -0.01376634  0.5663744 ]\n [ 0.43869567 -0.01376634  0.5663744 ]]", "desired_goal": "[[ 0.41305572 -1.7139128  -1.4545623 ]\n [ 0.8017363  -0.86438495 -1.2315738 ]\n [ 1.0682741  -0.7643425   0.5344247 ]\n [-1.0824602  -0.01711453 -0.74376804]]", "observation": "[[ 0.43869567 -0.01376634  0.5663744   0.0097507  -0.00362757  0.00383561]\n [ 0.43869567 -0.01376634  0.5663744   0.0097507  -0.00362757  0.00383561]\n [ 0.43869567 -0.01376634  0.5663744   0.0097507  -0.00362757  0.00383561]\n [ 0.43869567 -0.01376634  0.5663744   0.0097507  -0.00362757  0.00383561]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[ 0.0937257   0.08934379  0.1315598 ]\n [ 0.08749653 -0.01008056  0.12238649]\n [-0.08935452 -0.09130254  0.2831508 ]\n [ 0.07884658  0.07835177  0.23704241]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.15.0-52-generic-x86_64-with-glibc2.29 # 58~20.04.1-Ubuntu SMP Thu Oct 13 13:09:46 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.8.0", "PyTorch": "1.12.1", "GPU Enabled": "True", "Numpy": "1.23.4", "Gym": "0.21.0"}}

results.json

@@ -1 +1 @@
-{"mean_reward": -22.39945497252047, "std_reward": 7.728387906934784, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-26T07:34:11.330249"}
+{"mean_reward": -1.3904236593749375, "std_reward": 0.2952379570029963, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-26T08:14:08.911990"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0b7213a8b7e785255bf4a3e3000ce66d2d20f96e74541d573c061fb22102e912
+oid sha256:b57a9d35cc78674e4469adb94cd51e731509c71b0e0c919b53d378cced00297a
 size 2381