Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -28.87 +/- 9.08
+      value: -4.53 +/- 1.41
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b682918a83b7421555288e7ee9b343774b9d94f4b96998870042337834547ee9
-size 102578
+oid sha256:05cf5a4c4ea35ad7661144f13ce2411b9eb4b26481c89cb711bb5c4157d2b602
+size 108113

a2c-PandaReachDense-v2/data

@@ -41,12 +41,12 @@
         "_np_random": null
     },
     "n_envs": 4,
-    "num_timesteps": 0,
-    "_total_timesteps": 1000000,
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1680019628078374504,
+    "start_time": 1680019668573106422,
     "learning_rate": 0.0007,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -55,10 +55,10 @@
     },
     "_last_obs": {
         ":type:": "<class 'collections.OrderedDict'>",
-        ":serialized:": "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",
-        "achieved_goal": "[[0.22247829 0.01873506 0.6691021 ]\n [0.22247829 0.01873506 0.6691021 ]\n [0.22247829 0.01873506 0.6691021 ]\n [0.22247829 0.01873506 0.6691021 ]]",
-        "desired_goal": "[[ 0.27672517  0.21552333  0.3641494 ]\n [-0.85769725 -1.2858627   1.044527  ]\n [ 0.2816755   1.2708344   0.6180363 ]\n [ 0.57718074  0.9780342   1.4369578 ]]",
-        "observation": "[[0.22247829 0.01873506 0.6691021  0.01660685 0.00547355 0.03145329]\n [0.22247829 0.01873506 0.6691021  0.01660685 0.00547355 0.03145329]\n [0.22247829 0.01873506 0.6691021  0.01660685 0.00547355 0.03145329]\n [0.22247829 0.01873506 0.6691021  0.01660685 0.00547355 0.03145329]]"
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.5034177  -0.05298766  0.70144475]\n [ 0.5034177  -0.05298766  0.70144475]\n [ 0.5034177  -0.05298766  0.70144475]\n [ 0.5034177  -0.05298766  0.70144475]]",
+        "desired_goal": "[[-0.12512973  0.01022269  1.2859222 ]\n [ 0.86427647  1.4399704   0.24018486]\n [-1.4790723  -1.3204076  -1.5985231 ]\n [ 0.40475205 -0.11185182 -1.1026719 ]]",
+        "observation": "[[ 5.0341767e-01 -5.2987657e-02  7.0144475e-01  1.1370369e-02\n  -4.2495428e-04  2.0791080e-02]\n [ 5.0341767e-01 -5.2987657e-02  7.0144475e-01  1.1370369e-02\n  -4.2495428e-04  2.0791080e-02]\n [ 5.0341767e-01 -5.2987657e-02  7.0144475e-01  1.1370369e-02\n  -4.2495428e-04  2.0791080e-02]\n [ 5.0341767e-01 -5.2987657e-02  7.0144475e-01  1.1370369e-02\n  -4.2495428e-04  2.0791080e-02]]"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -66,9 +66,9 @@
     },
     "_last_original_obs": {
         ":type:": "<class 'collections.OrderedDict'>",
-        ":serialized:": "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",
+        ":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": "[[ 2.49759108e-02  9.72807258e-02  2.61498302e-01]\n [-1.33785352e-01  2.59660737e-04  2.86539286e-01]\n [ 1.12087503e-01 -1.37884542e-01  3.16569582e-02]\n [-1.20488562e-01  4.15950678e-02  1.42880725e-02]]",
+        "desired_goal": "[[ 0.11809266 -0.07596771  0.03663679]\n [ 0.06276048 -0.06224713  0.00543769]\n [-0.10949016  0.11355618  0.10691493]\n [-0.12457284  0.05607181  0.25982606]]",
         "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,
@@ -77,13 +77,13 @@
     "_current_progress_remaining": 0.0,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 50000,
+    "_n_updates": 100000,
     "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:d4b1bc06e068a78ace41b4a22008746067f65f8adcc73b0851a6e59c8837b053
+oid sha256:fc7f0135d96eb676d39e564417fc42b8ef2d197d2277d17f1746c4d3b7ac07e8
 size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:463f3cf1901196107c18956f287a567e0bc5e4e5cb2eab0d42313a2e713cf170
+oid sha256:662a0787e8a3d85aba58c77aa9b301f1e3821aba90f864f46f183cb0c535d440
 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 0x7fdf0eceac10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fdf0ecec480>"}, "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}}, "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, "num_timesteps": 0, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680019628078374504, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.22247829 0.01873506 0.6691021 ]\n [0.22247829 0.01873506 0.6691021 ]\n [0.22247829 0.01873506 0.6691021 ]\n [0.22247829 0.01873506 0.6691021 ]]", "desired_goal": "[[ 0.27672517  0.21552333  0.3641494 ]\n [-0.85769725 -1.2858627   1.044527  ]\n [ 0.2816755   1.2708344   0.6180363 ]\n [ 0.57718074  0.9780342   1.4369578 ]]", "observation": "[[0.22247829 0.01873506 0.6691021  0.01660685 0.00547355 0.03145329]\n [0.22247829 0.01873506 0.6691021  0.01660685 0.00547355 0.03145329]\n [0.22247829 0.01873506 0.6691021  0.01660685 0.00547355 0.03145329]\n [0.22247829 0.01873506 0.6691021  0.01660685 0.00547355 0.03145329]]"}, "_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": "[[ 2.49759108e-02  9.72807258e-02  2.61498302e-01]\n [-1.33785352e-01  2.59660737e-04  2.86539286e-01]\n [ 1.12087503e-01 -1.37884542e-01  3.16569582e-02]\n [-1.20488562e-01  4.15950678e-02  1.42880725e-02]]", "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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "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 0x7fdf0eceac10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fdf0ecec480>"}, "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}}, "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, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680019668573106422, "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": "[[ 0.5034177  -0.05298766  0.70144475]\n [ 0.5034177  -0.05298766  0.70144475]\n [ 0.5034177  -0.05298766  0.70144475]\n [ 0.5034177  -0.05298766  0.70144475]]", "desired_goal": "[[-0.12512973  0.01022269  1.2859222 ]\n [ 0.86427647  1.4399704   0.24018486]\n [-1.4790723  -1.3204076  -1.5985231 ]\n [ 0.40475205 -0.11185182 -1.1026719 ]]", "observation": "[[ 5.0341767e-01 -5.2987657e-02  7.0144475e-01  1.1370369e-02\n  -4.2495428e-04  2.0791080e-02]\n [ 5.0341767e-01 -5.2987657e-02  7.0144475e-01  1.1370369e-02\n  -4.2495428e-04  2.0791080e-02]\n [ 5.0341767e-01 -5.2987657e-02  7.0144475e-01  1.1370369e-02\n  -4.2495428e-04  2.0791080e-02]\n [ 5.0341767e-01 -5.2987657e-02  7.0144475e-01  1.1370369e-02\n  -4.2495428e-04  2.0791080e-02]]"}, "_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": "[[ 0.11809266 -0.07596771  0.03663679]\n [ 0.06276048 -0.06224713  0.00543769]\n [-0.10949016  0.11355618  0.10691493]\n [-0.12457284  0.05607181  0.25982606]]", "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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 100000, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -1 +1 @@
-{"mean_reward": -28.873975099995732, "std_reward": 9.076713522301212, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-28T16:07:16.041220"}
+{"mean_reward": -4.528843092080206, "std_reward": 1.4113941079120575, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-28T17:38:30.702417"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:99b2cb81e379d7c02d5408a2f62b99b0c7f550810fbb7ebe5f16b192d377f451
-size 3043
+oid sha256:5d98af18f0e1f65a227047b9fe194a4726b0367a78feabc93499ed4b6a65456c
+size 3056