Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -1.37 +/- 0.65
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-PandaReachDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1a2559a3ad57bc6d89e4fbcf7615d9f73afa58efb1f7a80a6e9de887fb44de5
+size 108136

a2c-PandaReachDense-v2/_stable_baselines3_version

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

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 0x7ffa9c44c670>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ffa9c44bb40>"
+    },
+    "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:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "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": 1679209798847287802,
+    "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": "[[3.9230168e-01 2.8879417e-04 5.6029105e-01]\n [3.9230168e-01 2.8879417e-04 5.6029105e-01]\n [3.9230168e-01 2.8879417e-04 5.6029105e-01]\n [3.9230168e-01 2.8879417e-04 5.6029105e-01]]",
+        "desired_goal": "[[-1.3304919  -0.1398738  -0.59725606]\n [-0.9962102   0.21819915 -0.9437623 ]\n [-0.21156986  0.01142158 -1.12005   ]\n [ 1.3036786  -0.50013816 -1.2373036 ]]",
+        "observation": "[[ 3.9230168e-01  2.8879417e-04  5.6029105e-01  8.3952704e-03\n  -3.7560025e-03  6.8625371e-04]\n [ 3.9230168e-01  2.8879417e-04  5.6029105e-01  8.3952704e-03\n  -3.7560025e-03  6.8625371e-04]\n [ 3.9230168e-01  2.8879417e-04  5.6029105e-01  8.3952704e-03\n  -3.7560025e-03  6.8625371e-04]\n [ 3.9230168e-01  2.8879417e-04  5.6029105e-01  8.3952704e-03\n  -3.7560025e-03  6.8625371e-04]]"
+    },
+    "_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": "[[-0.08510599 -0.02805628  0.06223255]\n [ 0.07108836  0.01718987  0.20966545]\n [-0.12844582 -0.00283419  0.1027528 ]\n [-0.04095852 -0.08399732  0.2936665 ]]",
+        "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": 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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ceb0b453696e25ceb060a5b8124e3f021245bec2838303b9fdd933e196bd7deb
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3bc1e7fef51b446ffec164ed8cc293b62e8315aea08ad6eb30165de3ad794576
+size 46014

a2c-PandaReachDense-v2/pytorch_variables.pth

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

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- 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

config.json

@@ -0,0 +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 0x7ffa9c44c670>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ffa9c44bb40>"}, "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679209798847287802, "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": "[[3.9230168e-01 2.8879417e-04 5.6029105e-01]\n [3.9230168e-01 2.8879417e-04 5.6029105e-01]\n [3.9230168e-01 2.8879417e-04 5.6029105e-01]\n [3.9230168e-01 2.8879417e-04 5.6029105e-01]]", "desired_goal": "[[-1.3304919  -0.1398738  -0.59725606]\n [-0.9962102   0.21819915 -0.9437623 ]\n [-0.21156986  0.01142158 -1.12005   ]\n [ 1.3036786  -0.50013816 -1.2373036 ]]", "observation": "[[ 3.9230168e-01  2.8879417e-04  5.6029105e-01  8.3952704e-03\n  -3.7560025e-03  6.8625371e-04]\n [ 3.9230168e-01  2.8879417e-04  5.6029105e-01  8.3952704e-03\n  -3.7560025e-03  6.8625371e-04]\n [ 3.9230168e-01  2.8879417e-04  5.6029105e-01  8.3952704e-03\n  -3.7560025e-03  6.8625371e-04]\n [ 3.9230168e-01  2.8879417e-04  5.6029105e-01  8.3952704e-03\n  -3.7560025e-03  6.8625371e-04]]"}, "_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": "[[-0.08510599 -0.02805628  0.06223255]\n [ 0.07108836  0.01718987  0.20966545]\n [-0.12844582 -0.00283419  0.1027528 ]\n [-0.04095852 -0.08399732  0.2936665 ]]", "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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIu9HHfEBg8L+UhpRSlIwBbJRLMowBdJRHQKhASBWgezV1fZQoaAZoCWgPQwguHAjJAqbxv5SGlFKUaBVLMmgWR0CoP/F7D2rXdX2UKGgGaAloD0MIHZPF/Udm8r+UhpRSlGgVSzJoFkdAqD+u36Q/5nV9lChoBmgJaA9DCEWDFDyFXOm/lIaUUpRoFUsyaBZHQKg/cJyhi9Z1fZQoaAZoCWgPQwhm3NRA87nyv5SGlFKUaBVLMmgWR0CoQVVwo9cKdX2UKGgGaAloD0MIO420VN5O/7+UhpRSlGgVSzJoFkdAqED+3vx6OnV9lChoBmgJaA9DCO2fpwGDZPm/lIaUUpRoFUsyaBZHQKhAvG6wt8N1fZQoaAZoCWgPQwgldJfEWXEDwJSGlFKUaBVLMmgWR0CoQH5ssQNDdX2UKGgGaAloD0MIMEs7NZfb9L+UhpRSlGgVSzJoFkdAqEJehwl0HXV9lChoBmgJaA9DCP89eO3SBv6/lIaUUpRoFUsyaBZHQKhCB9sJpnJ1fZQoaAZoCWgPQwiyoZv9gTIFwJSGlFKUaBVLMmgWR0CoQcUqpcX4dX2UKGgGaAloD0MIRYKpZtZS87+UhpRSlGgVSzJoFkdAqEGG6RQrMHV9lChoBmgJaA9DCM+FkV7ULvu/lIaUUpRoFUsyaBZHQKhDbYao/A11fZQoaAZoCWgPQwgktVAyOXX0v5SGlFKUaBVLMmgWR0CoQxbobGWEdX2UKGgGaAloD0MIzqj5Kvl4AsCUhpRSlGgVSzJoFkdAqELUTN+so3V9lChoBmgJaA9DCJQRF4BG6f6/lIaUUpRoFUsyaBZHQKhClgMMI/t1fZQoaAZoCWgPQwhy+KQTCabrv5SGlFKUaBVLMmgWR0CoRITzd1uBdX2UKGgGaAloD0MITYI3pFEhBsCUhpRSlGgVSzJoFkdAqEQuUt7KJXV9lChoBmgJaA9DCF0Y6UXtHgDAlIaUUpRoFUsyaBZHQKhD66V+qip1fZQoaAZoCWgPQwj7ljldFpP0v5SGlFKUaBVLMmgWR0CoQ61loUSJdX2UKGgGaAloD0MIFeXS+IUXAcCUhpRSlGgVSzJoFkdAqEWa7oSteXV9lChoBmgJaA9DCKzEPCtpxf+/lIaUUpRoFUsyaBZHQKhFRGax5cF1fZQoaAZoCWgPQwivQPSkTCr/v5SGlFKUaBVLMmgWR0CoRQHRb8m8dX2UKGgGaAloD0MI6Sec3Vpm8L+UhpRSlGgVSzJoFkdAqETD4YaYNXV9lChoBmgJaA9DCOf+6nHf6vW/lIaUUpRoFUsyaBZHQKhGttUGVzJ1fZQoaAZoCWgPQwjoaiv2l30AwJSGlFKUaBVLMmgWR0CoRmBtcfNidX2UKGgGaAloD0MIhXe5iO9E/b+UhpRSlGgVSzJoFkdAqEYeLHdXT3V9lChoBmgJaA9DCJIf8SvW8Pq/lIaUUpRoFUsyaBZHQKhF4A2hqTN1fZQoaAZoCWgPQwjJdyl1yRgBwJSGlFKUaBVLMmgWR0CoR/MdcSoPdX2UKGgGaAloD0MIWRe30QAe/7+UhpRSlGgVSzJoFkdAqEeckfLcK3V9lChoBmgJaA9DCJFhFW9kXvW/lIaUUpRoFUsyaBZHQKhHWgte2NN1fZQoaAZoCWgPQwhSYWwhyEHuv5SGlFKUaBVLMmgWR0CoRxv+wTufdX2UKGgGaAloD0MIDsAGRIhr/r+UhpRSlGgVSzJoFkdAqEkTijtXxXV9lChoBmgJaA9DCJur5jkiX/u/lIaUUpRoFUsyaBZHQKhIvQVsUIt1fZQoaAZoCWgPQwgYBiy5iqUAwJSGlFKUaBVLMmgWR0CoSHqHXVbzdX2UKGgGaAloD0MI4BCq1OwhAMCUhpRSlGgVSzJoFkdAqEg8Xxe9jHV9lChoBmgJaA9DCKpJ8IY06vK/lIaUUpRoFUsyaBZHQKhKQ6oVEeB1fZQoaAZoCWgPQwgqj26ERcXpv5SGlFKUaBVLMmgWR0CoSe0o8ZDRdX2UKGgGaAloD0MIL/zgfOpY6r+UhpRSlGgVSzJoFkdAqEmqrDIiknV9lChoBmgJaA9DCAOxbOaQVPi/lIaUUpRoFUsyaBZHQKhJbRSgoPV1fZQoaAZoCWgPQwixprIo7KLmv5SGlFKUaBVLMmgWR0CoS2pB5X2edX2UKGgGaAloD0MIrU1jey0oCcCUhpRSlGgVSzJoFkdAqEsTp3X7L3V9lChoBmgJaA9DCJtZSwFpnwHAlIaUUpRoFUsyaBZHQKhK0PIXCTF1fZQoaAZoCWgPQwjBGmfTEcD8v5SGlFKUaBVLMmgWR0CoSpK9wm3OdX2UKGgGaAloD0MITZ6ymq7n+7+UhpRSlGgVSzJoFkdAqEx0z0pVj3V9lChoBmgJaA9DCFdgyOpWz/y/lIaUUpRoFUsyaBZHQKhMHkKeCkJ1fZQoaAZoCWgPQwgWURN9Poryv5SGlFKUaBVLMmgWR0CoS9udXko4dX2UKGgGaAloD0MIR8hAnl0+/r+UhpRSlGgVSzJoFkdAqEudk8Rtg3V9lChoBmgJaA9DCDiDv1/MVvu/lIaUUpRoFUsyaBZHQKhNlLWZqmF1fZQoaAZoCWgPQwjE7GXbaQsHwJSGlFKUaBVLMmgWR0CoTT40uUUxdX2UKGgGaAloD0MI7WZGPxpO7r+UhpRSlGgVSzJoFkdAqEz7vJA+p3V9lChoBmgJaA9DCIRFRZxOMgbAlIaUUpRoFUsyaBZHQKhMvYe1a4d1fZQoaAZoCWgPQwhd4sgDkYUEwJSGlFKUaBVLMmgWR0CoTrKnNxEOdX2UKGgGaAloD0MIqdkDrcAQ8r+UhpRSlGgVSzJoFkdAqE5cFUyYX3V9lChoBmgJaA9DCGkc6ndh6wLAlIaUUpRoFUsyaBZHQKhOGY4Qz1t1fZQoaAZoCWgPQwhKKej2kkb7v5SGlFKUaBVLMmgWR0CoTdtrj5sTdX2UKGgGaAloD0MI7E/icyeY97+UhpRSlGgVSzJoFkdAqE/U03wTd3V9lChoBmgJaA9DCNzwu+mWHfS/lIaUUpRoFUsyaBZHQKhPflS0jTt1fZQoaAZoCWgPQwjMKmwGuAAGwJSGlFKUaBVLMmgWR0CoTzvB7/n4dX2UKGgGaAloD0MIpfRMLzE2BcCUhpRSlGgVSzJoFkdAqE79f/m1Y3V9lChoBmgJaA9DCBpR2ht8YQPAlIaUUpRoFUsyaBZHQKhQ8qSX+l11fZQoaAZoCWgPQwi2vkhoy3n3v5SGlFKUaBVLMmgWR0CoUJwGfPHDdX2UKGgGaAloD0MI/IwLB0Ly/L+UhpRSlGgVSzJoFkdAqFBZckdFOXV9lChoBmgJaA9DCH5Uw35PLOC/lIaUUpRoFUsyaBZHQKhQGzOX3QF1fZQoaAZoCWgPQwiskPKTah8AwJSGlFKUaBVLMmgWR0CoUhYW+GoKdX2UKGgGaAloD0MI4dHGEWvx5r+UhpRSlGgVSzJoFkdAqFG/axoqTnV9lChoBmgJaA9DCJD2P8Batfq/lIaUUpRoFUsyaBZHQKhRfOoHcDd1fZQoaAZoCWgPQwjUfQBSm/j6v5SGlFKUaBVLMmgWR0CoUT7FsHjZdX2UKGgGaAloD0MITzxnCwgt77+UhpRSlGgVSzJoFkdAqFMw5myxA3V9lChoBmgJaA9DCHoaMEj6NOO/lIaUUpRoFUsyaBZHQKhS2nXumaZ1fZQoaAZoCWgPQwhB0xIroxHkv5SGlFKUaBVLMmgWR0CoUpgC4jKQdX2UKGgGaAloD0MIllrvN9px8r+UhpRSlGgVSzJoFkdAqFJZ8QZn+XV9lChoBmgJaA9DCC6PNSODXATAlIaUUpRoFUsyaBZHQKhUbbJwKjV1fZQoaAZoCWgPQwgz3IDPDwMDwJSGlFKUaBVLMmgWR0CoVBhmoR7JdX2UKGgGaAloD0MIbNECtK1m+L+UhpRSlGgVSzJoFkdAqFPWrCFbmnV9lChoBmgJaA9DCI83+S06WfS/lIaUUpRoFUsyaBZHQKhTmWRA8jl1fZQoaAZoCWgPQwiIvVDAdvD2v5SGlFKUaBVLMmgWR0CoVlflQuVYdX2UKGgGaAloD0MICwithy8T2L+UhpRSlGgVSzJoFkdAqFYCOWBz3nV9lChoBmgJaA9DCOkOYmcK3fK/lIaUUpRoFUsyaBZHQKhVwLbYbsF1fZQoaAZoCWgPQwjXoC+9/Tnsv5SGlFKUaBVLMmgWR0CoVYOIqLCOdX2UKGgGaAloD0MI74y2Koms8b+UhpRSlGgVSzJoFkdAqFg+9QGfPHV9lChoBmgJaA9DCJXx7zMunOy/lIaUUpRoFUsyaBZHQKhX6VJL/S91fZQoaAZoCWgPQwgFb0ijAufzv5SGlFKUaBVLMmgWR0CoV6ev6j33dX2UKGgGaAloD0MInGotzEI76b+UhpRSlGgVSzJoFkdAqFdq4rjHXHV9lChoBmgJaA9DCOBIoMGmjvG/lIaUUpRoFUsyaBZHQKhaM/rSmZV1fZQoaAZoCWgPQwjn+6nx0k3qv5SGlFKUaBVLMmgWR0CoWd6cy31BdX2UKGgGaAloD0MIbFz/rs9c8L+UhpRSlGgVSzJoFkdAqFmdD6WPcXV9lChoBmgJaA9DCHv2XKYmAf2/lIaUUpRoFUsyaBZHQKhZX/Q0GeN1fZQoaAZoCWgPQwifyJOka6btv5SGlFKUaBVLMmgWR0CoXCjhky1vdX2UKGgGaAloD0MIysUYWMdx4r+UhpRSlGgVSzJoFkdAqFvTkIX0oXV9lChoBmgJaA9DCJBoAkUsAgnAlIaUUpRoFUsyaBZHQKhbkldC3PR1fZQoaAZoCWgPQwhBDd/CunHiv5SGlFKUaBVLMmgWR0CoW1VWjoIOdX2UKGgGaAloD0MIesN95NYk77+UhpRSlGgVSzJoFkdAqF4os9SuQ3V9lChoBmgJaA9DCP6ZQXxgR/S/lIaUUpRoFUsyaBZHQKhd0yC4Bmx1fZQoaAZoCWgPQwhEMA4uHfPyv5SGlFKUaBVLMmgWR0CoXZFwtJ4CdX2UKGgGaAloD0MILCtNSkH387+UhpRSlGgVSzJoFkdAqF1UMuvll3V9lChoBmgJaA9DCINuL2mM1gbAlIaUUpRoFUsyaBZHQKhfuKeCkGl1fZQoaAZoCWgPQwhE3nL1Y1P1v5SGlFKUaBVLMmgWR0CoX2IrnTy8dX2UKGgGaAloD0MIkSxgArfu5L+UhpRSlGgVSzJoFkdAqF8frSmZVnV9lChoBmgJaA9DCOxrXWqEvv+/lIaUUpRoFUsyaBZHQKhe4aCtihF1ZS4="}, "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"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -1.374818338546902, "std_reward": 0.6518243000076474, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-19T08:03:21.025500"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d1639a20fb424fc642d2e23da95b896b0629e0ef9dd42332c76b04200045ed3
+size 3056