Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -8.26 +/- 3.01
+      value: -0.25 +/- 0.08
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1b2ef305656db37e2ba5bae1c7d52e517f1f38aaa1c748151be484e788969b45
-size 54966
+oid sha256:8d081b17640bd138c66ab22031a4ae30f376de880c3c333f6f6fd8a801c9cdd2
+size 113730

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 0x78f182aed510>",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7debffe45630>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x78f182ad3cc0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7debffe3e3c0>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -19,29 +19,50 @@
             "weight_decay": 0
         }
     },
-    "num_timesteps": 0,
-    "_total_timesteps": 0,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": null,
+    "start_time": 1690729464126582315,
     "learning_rate": 0.0007,
     "tensorboard_log": null,
     "lr_schedule": {
         ":type:": "<class 'function'>",
         ":serialized:": "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"
     },
-    "_last_obs": null,
-    "_last_episode_starts": null,
-    "_last_original_obs": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]]",
+        "desired_goal": "[[ 1.725038    0.22726786 -1.1031133 ]\n [-1.6439835   1.5855771   0.97963184]\n [ 0.22930883 -0.43216893  1.3137956 ]\n [-1.5076865  -0.4682145   1.3235723 ]\n [ 0.8459775   1.4636784   1.2029473 ]\n [ 0.0100851  -0.4600022   1.4966838 ]\n [ 1.453817   -1.6706179  -0.30461955]\n [-1.6678624   1.6544406   0.10907043]\n [-0.37415007  1.3682415  -0.13984986]\n [-0.76682985  1.6955814  -1.43991   ]\n [-1.4252224  -0.6683843   0.6641001 ]\n [-0.55535805 -0.59685045 -1.0209147 ]\n [ 1.5799388   0.34801155 -0.04422095]\n [-0.39849955  0.72023064 -0.5224886 ]\n [-0.06335635  1.5005987   0.317265  ]\n [-1.3194735   0.94980717 -0.6879725 ]]",
+        "observation": "[[ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAEBAQEBAQEBAQEBAQEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_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]\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]\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]\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]\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": "[[ 0.11332238 -0.04182276  0.21633014]\n [ 0.03773101 -0.11667608  0.2435601 ]\n [-0.09928873  0.00357321  0.05038764]\n [ 0.13653556 -0.14733     0.02240838]\n [-0.1037133   0.06462602  0.29747117]\n [ 0.07534641  0.07905951  0.23833302]\n [-0.12537797  0.04605253  0.00597367]\n [ 0.10421229 -0.0004546   0.1698677 ]\n [ 0.11001436  0.08266433  0.18644732]\n [-0.07704362  0.1314628   0.05909339]\n [ 0.06739999  0.11366985  0.2155176 ]\n [-0.08116916  0.00436662  0.23063122]\n [ 0.0444565  -0.0769909   0.06541461]\n [-0.13666569 -0.09190051  0.26337773]\n [-0.14280412  0.07400816  0.16146496]\n [ 0.07556158 -0.02330643  0.16614655]]",
+        "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]\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]\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]\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]\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": 1,
+    "_current_progress_remaining": 0.0,
     "_stats_window_size": 100,
-    "ep_info_buffer": null,
-    "ep_success_buffer": null,
-    "_n_updates": 0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 12500,
     "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 sha256:9c7b2e55b9d2524652dd7a7dba0a98d761f3ba58839455894af3d91cd644015c
-size 687
+oid sha256:64fa2d25e3cafa26594ea23b5460146c075d573801ed3d746f0080e53ab89ec5
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4ed704d8a9886c9f943b4357d6ef33b99e99589df824f54ae3d09f64d3b572fe
-size 45886
+oid sha256:23bb5111aa6ac1c99a222b7c46ba6c855388909c7827c5080e370567633bb18d
+size 46014

a2c-PandaReachDense-v2/system_info.txt

@@ -2,6 +2,6 @@
 - Python: 3.10.6
 - Stable-Baselines3: 1.8.0
 - PyTorch: 2.0.1+cu118
-- GPU Enabled: False
+- GPU Enabled: True
 - Numpy: 1.22.4
 - Gym: 0.21.0

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 0x78f182aed510>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78f182ad3cc0>"}, "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": 0, "_total_timesteps": 0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": null, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": null, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 1, "_stats_window_size": 100, "ep_info_buffer": null, "ep_success_buffer": null, "_n_updates": 0, "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": 16, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "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 0x7debffe45630>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7debffe3e3c0>"}, "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": 1690729464126582315, "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": "[[ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]\n [ 0.42011902 -0.01789279  0.53080916]]", "desired_goal": "[[ 1.725038    0.22726786 -1.1031133 ]\n [-1.6439835   1.5855771   0.97963184]\n [ 0.22930883 -0.43216893  1.3137956 ]\n [-1.5076865  -0.4682145   1.3235723 ]\n [ 0.8459775   1.4636784   1.2029473 ]\n [ 0.0100851  -0.4600022   1.4966838 ]\n [ 1.453817   -1.6706179  -0.30461955]\n [-1.6678624   1.6544406   0.10907043]\n [-0.37415007  1.3682415  -0.13984986]\n [-0.76682985  1.6955814  -1.43991   ]\n [-1.4252224  -0.6683843   0.6641001 ]\n [-0.55535805 -0.59685045 -1.0209147 ]\n [ 1.5799388   0.34801155 -0.04422095]\n [-0.39849955  0.72023064 -0.5224886 ]\n [-0.06335635  1.5005987   0.317265  ]\n [-1.3194735   0.94980717 -0.6879725 ]]", "observation": "[[ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]\n [ 0.42011902 -0.01789279  0.53080916 -0.00349215 -0.0033218   0.00580474]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAEBAQEBAQEBAQEBAQEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_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]\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]\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]\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]\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": "[[ 0.11332238 -0.04182276  0.21633014]\n [ 0.03773101 -0.11667608  0.2435601 ]\n [-0.09928873  0.00357321  0.05038764]\n [ 0.13653556 -0.14733     0.02240838]\n [-0.1037133   0.06462602  0.29747117]\n [ 0.07534641  0.07905951  0.23833302]\n [-0.12537797  0.04605253  0.00597367]\n [ 0.10421229 -0.0004546   0.1698677 ]\n [ 0.11001436  0.08266433  0.18644732]\n [-0.07704362  0.1314628   0.05909339]\n [ 0.06739999  0.11366985  0.2155176 ]\n [-0.08116916  0.00436662  0.23063122]\n [ 0.0444565  -0.0769909   0.06541461]\n [-0.13666569 -0.09190051  0.26337773]\n [-0.14280412  0.07400816  0.16146496]\n [ 0.07556158 -0.02330643  0.16614655]]", "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]\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]\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]\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]\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": 12500, "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:": "gAWVcwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUjAFDlHSUUpSMBGhpZ2iUaBMolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgLSwOFlGgWdJRSlIwNYm91bmRlZF9iZWxvd5RoEyiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYDAAAAAAAAAAEBAZRoIksDhZRoFnSUUpSMCl9ucF9yYW5kb22UTnViLg==", "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": 16, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -1 +1 @@
-{"mean_reward": -8.256617883965372, "std_reward": 3.005049078367665, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-30T10:53:59.277979"}
+{"mean_reward": -0.2535193673887989, "std_reward": 0.07654787707000199, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-30T15:45:08.738671"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:90bd3d0d18e5d143d4a31c49f50889d0873e599738425797981b5a2688651939
-size 2374
+oid sha256:29b0895ed47749615970f7c55de6d88b895fb3368a00fe48988d24e808f923e7
+size 2387