Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -3.76 +/- 0.81
+      value: -0.47 +/- 0.18
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2-2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:026dd52106c35ed72f24891534d9cf953748b20f79fafd3afa1e99995ca7c0dc
+size 109532

a2c-PandaReachDense-v2-2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-PandaReachDense-v2-2/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7f360b7ad790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f360b7a78d0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "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": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677609274745926589,
+    "learning_rate": 0.00096,
+    "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": "[[ 0.42209968 -0.00689183  0.5423159 ]\n [ 0.42209968 -0.00689183  0.5423159 ]\n [ 0.42209968 -0.00689183  0.5423159 ]\n [ 0.42209968 -0.00689183  0.5423159 ]]",
+        "desired_goal": "[[ 1.6526102   1.1273532   0.60232615]\n [ 1.5418316  -0.78655857 -1.1105704 ]\n [ 1.063899    0.23377208 -1.5786961 ]\n [ 1.7080808  -1.1451175   0.9648641 ]]",
+        "observation": "[[ 0.42209968 -0.00689183  0.5423159   0.07903544 -0.00157409  0.06399492]\n [ 0.42209968 -0.00689183  0.5423159   0.07903544 -0.00157409  0.06399492]\n [ 0.42209968 -0.00689183  0.5423159   0.07903544 -0.00157409  0.06399492]\n [ 0.42209968 -0.00689183  0.5423159   0.07903544 -0.00157409  0.06399492]]"
+    },
+    "_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": "[[-0.08109855  0.08102187  0.27999434]\n [ 0.00650617  0.12056398  0.11431679]\n [ 0.02361749 -0.12391396  0.07667905]\n [-0.06504031 -0.11506915  0.21807058]]",
+        "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": true,
+    "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": 31250,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-PandaReachDense-v2-2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0cd25f6ddcb7e2f4e0842d21033d69538dab68b1294dd668fc9bfba183694a69
+size 45438

a2c-PandaReachDense-v2-2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:248cd7a05cf07ee09447ea21208ff5784a0d20c23b782d8fe9d47b09c075933e
+size 46718

a2c-PandaReachDense-v2-2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v2-2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- 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 0x7f38aad2edc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f38aad2b780>"}, "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": 1677601156637382749, "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": "[[ 0.403972   -0.00877641  0.5586826 ]\n [ 0.403972   -0.00877641  0.5586826 ]\n [ 0.403972   -0.00877641  0.5586826 ]\n [ 0.403972   -0.00877641  0.5586826 ]]", "desired_goal": "[[ 0.98201025  0.15238182 -0.26572365]\n [-0.9025177   1.2322385   1.1208248 ]\n [ 1.2766442   1.6184238  -0.93945336]\n [-1.4789432   0.77973324  1.4916817 ]]", "observation": "[[ 4.0397200e-01 -8.7764133e-03  5.5868262e-01 -7.1061966e-03\n  -2.2940797e-04  1.7887121e-02]\n [ 4.0397200e-01 -8.7764133e-03  5.5868262e-01 -7.1061966e-03\n  -2.2940797e-04  1.7887121e-02]\n [ 4.0397200e-01 -8.7764133e-03  5.5868262e-01 -7.1061966e-03\n  -2.2940797e-04  1.7887121e-02]\n [ 4.0397200e-01 -8.7764133e-03  5.5868262e-01 -7.1061966e-03\n  -2.2940797e-04  1.7887121e-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": "[[ 0.02276148  0.10291839  0.15553556]\n [-0.14257836 -0.00888067  0.17180103]\n [-0.11481243 -0.11381625  0.17339379]\n [-0.03904455  0.13648853  0.13675343]]", "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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "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 0x7f360b7ad790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f360b7a78d0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677609274745926589, "learning_rate": 0.00096, "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": "[[ 0.42209968 -0.00689183  0.5423159 ]\n [ 0.42209968 -0.00689183  0.5423159 ]\n [ 0.42209968 -0.00689183  0.5423159 ]\n [ 0.42209968 -0.00689183  0.5423159 ]]", "desired_goal": "[[ 1.6526102   1.1273532   0.60232615]\n [ 1.5418316  -0.78655857 -1.1105704 ]\n [ 1.063899    0.23377208 -1.5786961 ]\n [ 1.7080808  -1.1451175   0.9648641 ]]", "observation": "[[ 0.42209968 -0.00689183  0.5423159   0.07903544 -0.00157409  0.06399492]\n [ 0.42209968 -0.00689183  0.5423159   0.07903544 -0.00157409  0.06399492]\n [ 0.42209968 -0.00689183  0.5423159   0.07903544 -0.00157409  0.06399492]\n [ 0.42209968 -0.00689183  0.5423159   0.07903544 -0.00157409  0.06399492]]"}, "_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": "[[-0.08109855  0.08102187  0.27999434]\n [ 0.00650617  0.12056398  0.11431679]\n [ 0.02361749 -0.12391396  0.07667905]\n [-0.06504031 -0.11506915  0.21807058]]", "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": true, "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": 31250, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "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": -3.757531937491149, "std_reward": 0.8052531426857129, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-28T17:59:21.955614"}
+{"mean_reward": -0.46529111828422176, "std_reward": 0.1798840814487308, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-28T19:29:24.381916"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d8b1352687af37ed1f979dc8c04dc302961251488a58568c67b4572db8a666a8
+oid sha256:2f432e592861fc853d913c7024fa2aff677cd5aa858cb101ab56ceb0a9e9323a
 size 3056