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: -0.41 +/- 0.11
+      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 sha256:6deb4837dde7204efcb9173f83589933767b6765b56b16f567ce95ebb630d1bc
+size 108011

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 0x7f28d61b7790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f28d61aede0>"
+    },
+    "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": 1676838963823671512,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[0.41296262 0.00205239 0.5565705 ]\n [0.41296262 0.00205239 0.5565705 ]\n [0.41296262 0.00205239 0.5565705 ]\n [0.41296262 0.00205239 0.5565705 ]]",
+        "desired_goal": "[[-0.10950382 -0.37669653  0.3014613 ]\n [ 0.31280142 -0.8234254  -0.77185214]\n [ 1.595042    0.27987608  0.62152123]\n [-0.80318946 -1.0644693  -1.4721389 ]]",
+        "observation": "[[ 0.41296262  0.00205239  0.5565705   0.01362431 -0.00212004  0.00352987]\n [ 0.41296262  0.00205239  0.5565705   0.01362431 -0.00212004  0.00352987]\n [ 0.41296262  0.00205239  0.5565705   0.01362431 -0.00212004  0.00352987]\n [ 0.41296262  0.00205239  0.5565705   0.01362431 -0.00212004  0.00352987]]"
+    },
+    "_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": "[[-0.02278472 -0.01787067  0.2832482 ]\n [ 0.12510054  0.02714348  0.13398126]\n [ 0.00598932  0.13371855  0.08974185]\n [-0.08995264  0.0626804   0.22975959]]",
+        "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 sha256:88a8cae20de283bc2b4dfdc31aee85365a7c79ff7002f1a6f595e5ccd17caa39
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9844fc7e662a5c1daaa6f1cde195496edf01cfecdb1990405b08282fa87f7246
+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-glibc2.29 # 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: 1.21.6
+- 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 0x7f28d61b7790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f28d61aede0>"}, "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": 1676838963823671512, "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": "[[0.41296262 0.00205239 0.5565705 ]\n [0.41296262 0.00205239 0.5565705 ]\n [0.41296262 0.00205239 0.5565705 ]\n [0.41296262 0.00205239 0.5565705 ]]", "desired_goal": "[[-0.10950382 -0.37669653  0.3014613 ]\n [ 0.31280142 -0.8234254  -0.77185214]\n [ 1.595042    0.27987608  0.62152123]\n [-0.80318946 -1.0644693  -1.4721389 ]]", "observation": "[[ 0.41296262  0.00205239  0.5565705   0.01362431 -0.00212004  0.00352987]\n [ 0.41296262  0.00205239  0.5565705   0.01362431 -0.00212004  0.00352987]\n [ 0.41296262  0.00205239  0.5565705   0.01362431 -0.00212004  0.00352987]\n [ 0.41296262  0.00205239  0.5565705   0.01362431 -0.00212004  0.00352987]]"}, "_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": "[[-0.02278472 -0.01787067  0.2832482 ]\n [ 0.12510054  0.02714348  0.13398126]\n [ 0.00598932  0.13371855  0.08974185]\n [-0.08995264  0.0626804   0.22975959]]", "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////9LAHSUYkMI7N6KxAS14L+UhpRSlIwBbJRLMowBdJRHQKgE5Sqlxfh1fZQoaAZoCWgPQwg6CDpa1RLgv5SGlFKUaBVLMmgWR0CoBKiXhOxjdX2UKGgGaAloD0MIYmU08nlF57+UhpRSlGgVSzJoFkdAqARtZxJd0XV9lChoBmgJaA9DCDp3u16aouW/lIaUUpRoFUsyaBZHQKgEMkcCHRF1fZQoaAZoCWgPQwig/rPmx1/iv5SGlFKUaBVLMmgWR0CoBiyWRigCdX2UKGgGaAloD0MIigCnd/F+27+UhpRSlGgVSzJoFkdAqAXwA4n4PHV9lChoBmgJaA9DCEW5NH7hlei/lIaUUpRoFUsyaBZHQKgFtOIInjR1fZQoaAZoCWgPQwgdHOxNDMnlv5SGlFKUaBVLMmgWR0CoBXp3xFy8dX2UKGgGaAloD0MIB3k9mBSf4r+UhpRSlGgVSzJoFkdAqAdKFwkxAXV9lChoBmgJaA9DCMxh9x3DY+O/lIaUUpRoFUsyaBZHQKgHDWKdhAp1fZQoaAZoCWgPQwhDOdGuQsrjv5SGlFKUaBVLMmgWR0CoBtJkoWpIdX2UKGgGaAloD0MIYviImBLJ47+UhpRSlGgVSzJoFkdAqAaXLFGXonV9lChoBmgJaA9DCFcHQNzVq9O/lIaUUpRoFUsyaBZHQKgIaqDsdDJ1fZQoaAZoCWgPQwiFsYUgB6Xuv5SGlFKUaBVLMmgWR0CoCC33QD3edX2UKGgGaAloD0MI1dAGYAMi6r+UhpRSlGgVSzJoFkdAqAfzAeq7y3V9lChoBmgJaA9DCHef46PFGea/lIaUUpRoFUsyaBZHQKgHuAQQL/l1fZQoaAZoCWgPQwioqzsW2yThv5SGlFKUaBVLMmgWR0CoCYpgCwKTdX2UKGgGaAloD0MI0NOAQdIn4r+UhpRSlGgVSzJoFkdAqAlN1+y7gHV9lChoBmgJaA9DCFgfD313K9q/lIaUUpRoFUsyaBZHQKgJEuPmxMZ1fZQoaAZoCWgPQwjNkCqKV9nlv5SGlFKUaBVLMmgWR0CoCNfFR51OdX2UKGgGaAloD0MI7UW0HVN33L+UhpRSlGgVSzJoFkdAqAqooRZlnXV9lChoBmgJaA9DCMgHPZtVn+a/lIaUUpRoFUsyaBZHQKgKa94/u9h1fZQoaAZoCWgPQwjogvqWOd3gv5SGlFKUaBVLMmgWR0CoCjDEvTPTdX2UKGgGaAloD0MIdF/ObFfo2L+UhpRSlGgVSzJoFkdAqAn1dLQHA3V9lChoBmgJaA9DCGABTBk4oM+/lIaUUpRoFUsyaBZHQKgLzcgyM1l1fZQoaAZoCWgPQwjgn1Ilyl7ov5SGlFKUaBVLMmgWR0CoC5FdcB2fdX2UKGgGaAloD0MIUmNCzCVV17+UhpRSlGgVSzJoFkdAqAtW/cnE23V9lChoBmgJaA9DCKcExCRcSOe/lIaUUpRoFUsyaBZHQKgLHDjzZpV1fZQoaAZoCWgPQwjPL0rQX2jkv5SGlFKUaBVLMmgWR0CoDOQBgeA/dX2UKGgGaAloD0MIRX9o5sk11L+UhpRSlGgVSzJoFkdAqAynYHxBmnV9lChoBmgJaA9DCAXfNH12wNi/lIaUUpRoFUsyaBZHQKgMbFLFn7J1fZQoaAZoCWgPQwh5zEBl/Pvfv5SGlFKUaBVLMmgWR0CoDDEr5IpZdX2UKGgGaAloD0MIibFMv0S867+UhpRSlGgVSzJoFkdAqA3/JV81GnV9lChoBmgJaA9DCKdaC7PQTuK/lIaUUpRoFUsyaBZHQKgNwqlP8AJ1fZQoaAZoCWgPQwh2/1iIDsHzv5SGlFKUaBVLMmgWR0CoDYe6Zpi7dX2UKGgGaAloD0MIWRe30QDe1b+UhpRSlGgVSzJoFkdAqA1MmD15B3V9lChoBmgJaA9DCEeP39v0Z96/lIaUUpRoFUsyaBZHQKgPIrIYFaB1fZQoaAZoCWgPQwiMg0vHnGfOv5SGlFKUaBVLMmgWR0CoDuYOc2BKdX2UKGgGaAloD0MILekoB7MJ1b+UhpRSlGgVSzJoFkdAqA6rG3nZCnV9lChoBmgJaA9DCML3/gbtVee/lIaUUpRoFUsyaBZHQKgOb/LDAJt1fZQoaAZoCWgPQwiCHmrbMIrmv5SGlFKUaBVLMmgWR0CoED+PikwfdX2UKGgGaAloD0MIMh8Q6EzazL+UhpRSlGgVSzJoFkdAqBAD/MnqmnV9lChoBmgJaA9DCGouNxjqMOu/lIaUUpRoFUsyaBZHQKgPya+evp11fZQoaAZoCWgPQwgjowOSsG/Uv5SGlFKUaBVLMmgWR0CoD47NB4UvdX2UKGgGaAloD0MIV3bB4Jq747+UhpRSlGgVSzJoFkdAqBFa+FlCkXV9lChoBmgJaA9DCPjhICHKF9O/lIaUUpRoFUsyaBZHQKgRHlyR0U51fZQoaAZoCWgPQwgb2CrB4nDdv5SGlFKUaBVLMmgWR0CoEONSQ5mzdX2UKGgGaAloD0MIuMoTCDvF5b+UhpRSlGgVSzJoFkdAqBCoEnssx3V9lChoBmgJaA9DCP5itmRVhNa/lIaUUpRoFUsyaBZHQKgSfbWVeKN1fZQoaAZoCWgPQwijIHh8e9fOv5SGlFKUaBVLMmgWR0CoEkESuhbodX2UKGgGaAloD0MIfGKdKt+z4L+UhpRSlGgVSzJoFkdAqBIF0FKTS3V9lChoBmgJaA9DCOzBpPj4BOO/lIaUUpRoFUsyaBZHQKgRyouPFNt1fZQoaAZoCWgPQwjX9nZLcsDbv5SGlFKUaBVLMmgWR0CoE9rHuJDWdX2UKGgGaAloD0MIPZtVn6st4L+UhpRSlGgVSzJoFkdAqBOfT9bX6XV9lChoBmgJaA9DCG5S0Vj7O96/lIaUUpRoFUsyaBZHQKgTZPu5SWJ1fZQoaAZoCWgPQwilL4Sc9//Xv5SGlFKUaBVLMmgWR0CoEyrYGt6pdX2UKGgGaAloD0MIaRoUzQNY27+UhpRSlGgVSzJoFkdAqBXJjpcHGHV9lChoBmgJaA9DCINRSZ2ApuK/lIaUUpRoFUsyaBZHQKgVjdKujh11fZQoaAZoCWgPQwhQcRx4tdzUv5SGlFKUaBVLMmgWR0CoFVPCVKPGdX2UKGgGaAloD0MIXkpdMo6R17+UhpRSlGgVSzJoFkdAqBUZsuWa+nV9lChoBmgJaA9DCLqHhO/9DeO/lIaUUpRoFUsyaBZHQKgXtSOzY291fZQoaAZoCWgPQwg/NsmP+BXov5SGlFKUaBVLMmgWR0CoF3kpZwGXdX2UKGgGaAloD0MIKQezCTAs3b+UhpRSlGgVSzJoFkdAqBc+/gzguXV9lChoBmgJaA9DCBzQ0hVsI9C/lIaUUpRoFUsyaBZHQKgXBO+qR2d1fZQoaAZoCWgPQwgN5NnlWx/Tv5SGlFKUaBVLMmgWR0CoGa/8l5WzdX2UKGgGaAloD0MISRRa1v1j2L+UhpRSlGgVSzJoFkdAqBl0QmNR33V9lChoBmgJaA9DCPZ698d7Vey/lIaUUpRoFUsyaBZHQKgZOiHIp6R1fZQoaAZoCWgPQwjVJHhDGpXmv5SGlFKUaBVLMmgWR0CoGP/ukUKzdX2UKGgGaAloD0MI/yJozCRq4b+UhpRSlGgVSzJoFkdAqBuak0rK/3V9lChoBmgJaA9DCBdky/J1GeO/lIaUUpRoFUsyaBZHQKgbXvR7Z391fZQoaAZoCWgPQwgo1NNH4A/bv5SGlFKUaBVLMmgWR0CoGyU1IiC8dX2UKGgGaAloD0MI3e7lPjkK2r+UhpRSlGgVSzJoFkdAqBrrG7z06HV9lChoBmgJaA9DCLiQR3AjZdi/lIaUUpRoFUsyaBZHQKgdl9H+ZPV1fZQoaAZoCWgPQwgk06HT827Kv5SGlFKUaBVLMmgWR0CoHVxAB1cMdX2UKGgGaAloD0MIHch6avXV1r+UhpRSlGgVSzJoFkdAqB0ik/KQrHV9lChoBmgJaA9DCNodUgyQaNO/lIaUUpRoFUsyaBZHQKgc6Eg4ffZ1fZQoaAZoCWgPQwiWW1oNiXvZv5SGlFKUaBVLMmgWR0CoHvxZU1htdX2UKGgGaAloD0MIpwaaz7nb5r+UhpRSlGgVSzJoFkdAqB6/tY0VJ3V9lChoBmgJaA9DCNf34SAhytG/lIaUUpRoFUsyaBZHQKgehLM9r451fZQoaAZoCWgPQwiVgQNauoLDv5SGlFKUaBVLMmgWR0CoHkloL5RCdX2UKGgGaAloD0MIkUQvo1ju7L+UhpRSlGgVSzJoFkdAqCAfIMjNZHV9lChoBmgJaA9DCI6SV+cYkNW/lIaUUpRoFUsyaBZHQKgf4qfe1rt1fZQoaAZoCWgPQwiXVdgMcEHVv5SGlFKUaBVLMmgWR0CoH6ew9q1xdX2UKGgGaAloD0MIycwFLo810r+UhpRSlGgVSzJoFkdAqB9smjTKDHV9lChoBmgJaA9DCE60q5Dyk9y/lIaUUpRoFUsyaBZHQKghPSEUTL51fZQoaAZoCWgPQwgqxCPx8nTpv5SGlFKUaBVLMmgWR0CoIQCDujREdX2UKGgGaAloD0MICHdn7bYL2r+UhpRSlGgVSzJoFkdAqCDFix3V1HV9lChoBmgJaA9DCKvpeqLrQui/lIaUUpRoFUsyaBZHQKggimJFb3Z1fZQoaAZoCWgPQwhNh07Pu7HSv5SGlFKUaBVLMmgWR0CoIlOZb6gvdX2UKGgGaAloD0MIURToE3kS5L+UhpRSlGgVSzJoFkdAqCIW6K+BYnV9lChoBmgJaA9DCHv18dB3t96/lIaUUpRoFUsyaBZHQKgh28kleGB1fZQoaAZoCWgPQwj59NiWAWfYv5SGlFKUaBVLMmgWR0CoIaB/RVp9dX2UKGgGaAloD0MI+YIWEjC607+UhpRSlGgVSzJoFkdAqCN+lKsdUHV9lChoBmgJaA9DCKNbr+lBQdm/lIaUUpRoFUsyaBZHQKgjQg4ffXR1fZQoaAZoCWgPQwg4gem0bgPnv5SGlFKUaBVLMmgWR0CoIwcujASGdX2UKGgGaAloD0MI9tTqq6sC6b+UhpRSlGgVSzJoFkdAqCLMGPgeinV9lChoBmgJaA9DCBBdUN8yp+C/lIaUUpRoFUsyaBZHQKgkj77bcoJ1fZQoaAZoCWgPQwh2492RsVrnv5SGlFKUaBVLMmgWR0CoJFMmnfl7dX2UKGgGaAloD0MI0XgiiPNwzr+UhpRSlGgVSzJoFkdAqCQYJzDGcXV9lChoBmgJaA9DCG2P3nAfudq/lIaUUpRoFUsyaBZHQKgj3O1v2oN1ZS4="}, "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-glibc2.29 # 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": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.41147077072528193, "std_reward": 0.11051947240961725, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-19T21:27:38.093318"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71a88d1b237d5259b824b5c358362cdeb027e4d9fe3d269c9267da5d958a4469
+size 3056