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: -4.26 +/- 1.93
+      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:b90fc37b770295333ec5e08ebee5a24b373ebb2a50c2740d524b691600a946c1
+size 108366

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:": "gASVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__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 0x7f04e6c35560>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f04e6d5cd80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gASVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/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": 1679043684598491271,
+    "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": "[[ 0.4935391  -0.05768704  0.6617408 ]\n [ 0.4935391  -0.05768704  0.6617408 ]\n [ 0.4935391  -0.05768704  0.6617408 ]\n [ 0.4935391  -0.05768704  0.6617408 ]]",
+        "desired_goal": "[[-0.30551928 -1.1676615   0.23722856]\n [ 1.6602525   0.39142123  0.5006751 ]\n [ 0.13898027  0.30553263 -0.07045155]\n [ 0.264174   -0.96710503 -0.7195085 ]]",
+        "observation": "[[ 4.9353909e-01 -5.7687040e-02  6.6174078e-01 -2.2218917e-02\n  -4.6267844e-04 -6.7594163e-03]\n [ 4.9353909e-01 -5.7687040e-02  6.6174078e-01 -2.2218917e-02\n  -4.6267844e-04 -6.7594163e-03]\n [ 4.9353909e-01 -5.7687040e-02  6.6174078e-01 -2.2218917e-02\n  -4.6267844e-04 -6.7594163e-03]\n [ 4.9353909e-01 -5.7687040e-02  6.6174078e-01 -2.2218917e-02\n  -4.6267844e-04 -6.7594163e-03]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAEBAQGUdJRiLg=="
+    },
+    "_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": "[[ 0.0243088   0.01535481  0.28424343]\n [-0.03218404  0.09413277  0.11211932]\n [ 0.08882998 -0.08617806  0.02220434]\n [ 0.01325622  0.14477657  0.28822502]]",
+        "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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_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 sha256:70780f202b3eda31e63d7d3716544452840449f0836e46b2174f4aaf91f5e4a9
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f12b4037207b34e4bef0d4e2e09e2d6c52d6b5cce96b36780eb7e2febcf9098
+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-5.15.89+-x86_64-with-debian-bullseye-sid # 1 SMP Sat Mar 11 10:24:08 UTC 2023
+- Python: 3.7.12
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.0
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__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 0x7f04e6c35560>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f04e6d5cd80>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gASVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/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": 1679043684598491271, "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": "[[ 0.4935391  -0.05768704  0.6617408 ]\n [ 0.4935391  -0.05768704  0.6617408 ]\n [ 0.4935391  -0.05768704  0.6617408 ]\n [ 0.4935391  -0.05768704  0.6617408 ]]", "desired_goal": "[[-0.30551928 -1.1676615   0.23722856]\n [ 1.6602525   0.39142123  0.5006751 ]\n [ 0.13898027  0.30553263 -0.07045155]\n [ 0.264174   -0.96710503 -0.7195085 ]]", "observation": "[[ 4.9353909e-01 -5.7687040e-02  6.6174078e-01 -2.2218917e-02\n  -4.6267844e-04 -6.7594163e-03]\n [ 4.9353909e-01 -5.7687040e-02  6.6174078e-01 -2.2218917e-02\n  -4.6267844e-04 -6.7594163e-03]\n [ 4.9353909e-01 -5.7687040e-02  6.6174078e-01 -2.2218917e-02\n  -4.6267844e-04 -6.7594163e-03]\n [ 4.9353909e-01 -5.7687040e-02  6.6174078e-01 -2.2218917e-02\n  -4.6267844e-04 -6.7594163e-03]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAEBAQGUdJRiLg=="}, "_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": "[[ 0.0243088   0.01535481  0.28424343]\n [-0.03218404  0.09413277  0.11211932]\n [ 0.08882998 -0.08617806  0.02220434]\n [ 0.01325622  0.14477657  0.28822502]]", "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:": "gASVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIbECEuHK2BMCUhpRSlIwBbJRLMowBdJRHQKMvbxuKoAJ1fZQoaAZoCWgPQwj27o/3qrUXwJSGlFKUaBVLMmgWR0CjLykCvHLidX2UKGgGaAloD0MIMWDJVSw+GcCUhpRSlGgVSzJoFkdAoy7i9M9KVnV9lChoBmgJaA9DCAN5dvnWhwjAlIaUUpRoFUsyaBZHQKMunq1w5vN1fZQoaAZoCWgPQwi/RpIgXHESwJSGlFKUaBVLMmgWR0CjMEXZXdTHdX2UKGgGaAloD0MI1dAGYAPSFsCUhpRSlGgVSzJoFkdAoy//yPMjeXV9lChoBmgJaA9DCKWHodXJ6RbAlIaUUpRoFUsyaBZHQKMvubZOBUd1fZQoaAZoCWgPQwj1E85uLXMVwJSGlFKUaBVLMmgWR0CjL3VgYxcndX2UKGgGaAloD0MIXvWAecikEcCUhpRSlGgVSzJoFkdAozEZ86V+qnV9lChoBmgJaA9DCLx5qkNuZgTAlIaUUpRoFUsyaBZHQKMw0+qR2bJ1fZQoaAZoCWgPQwiPcjCbAEMNwJSGlFKUaBVLMmgWR0CjMI3I2fkFdX2UKGgGaAloD0MIoZ4+An/YCMCUhpRSlGgVSzJoFkdAozBJlYlpoXV9lChoBmgJaA9DCOM1r+qsNgjAlIaUUpRoFUsyaBZHQKMx8Pkq+al1fZQoaAZoCWgPQwg83A4Ni0ETwJSGlFKUaBVLMmgWR0CjMar2xptadX2UKGgGaAloD0MIdLLUer+BEsCUhpRSlGgVSzJoFkdAozFk6Lfk3nV9lChoBmgJaA9DCIWZtn9lJQLAlIaUUpRoFUsyaBZHQKMxIMG5c1R1fZQoaAZoCWgPQwij6exkcOQYwJSGlFKUaBVLMmgWR0CjMsqU/wAmdX2UKGgGaAloD0MI3e7lPjmqBMCUhpRSlGgVSzJoFkdAozKElme18nV9lChoBmgJaA9DCKZIvhJI6QHAlIaUUpRoFUsyaBZHQKMyPpxm03R1fZQoaAZoCWgPQwid9/9xwiQYwJSGlFKUaBVLMmgWR0CjMfpe3QUpdX2UKGgGaAloD0MIkBFQ4QjSCcCUhpRSlGgVSzJoFkdAozPFknTiKnV9lChoBmgJaA9DCFuzlZf8ByLAlIaUUpRoFUsyaBZHQKMzgRXfZVZ1fZQoaAZoCWgPQwhPsP86N90fwJSGlFKUaBVLMmgWR0CjMzxsVLzxdX2UKGgGaAloD0MIopbmVghrCMCUhpRSlGgVSzJoFkdAozL5DXvphXV9lChoBmgJaA9DCPtd2JqtvATAlIaUUpRoFUsyaBZHQKM1C2xY7q91fZQoaAZoCWgPQwgP1CmPboQFwJSGlFKUaBVLMmgWR0CjNMWuX/o8dX2UKGgGaAloD0MIQkC+hApuCMCUhpRSlGgVSzJoFkdAozSAA0bcXXV9lChoBmgJaA9DCAbZsnxddhLAlIaUUpRoFUsyaBZHQKM0O8XenAJ1fZQoaAZoCWgPQwgSE9TwLXwUwJSGlFKUaBVLMmgWR0CjNfkGRmsedX2UKGgGaAloD0MICmZMwRq3FMCUhpRSlGgVSzJoFkdAozWzVWjoIXV9lChoBmgJaA9DCAAAAAAAIBPAlIaUUpRoFUsyaBZHQKM1bU/fO2R1fZQoaAZoCWgPQwjdKLLWUEoGwJSGlFKUaBVLMmgWR0CjNSkqUeMidX2UKGgGaAloD0MIqaJ4lbXNDsCUhpRSlGgVSzJoFkdAozbeX1J173V9lChoBmgJaA9DCDf8brplpwzAlIaUUpRoFUsyaBZHQKM2mF5fMOh1fZQoaAZoCWgPQwgfLGNDN5sMwJSGlFKUaBVLMmgWR0CjNlJDE3sHdX2UKGgGaAloD0MIn+klxjKdE8CUhpRSlGgVSzJoFkdAozYOBOHnEHV9lChoBmgJaA9DCCRFZFjFuxrAlIaUUpRoFUsyaBZHQKM3uXQdCE91fZQoaAZoCWgPQwiveVVntSASwJSGlFKUaBVLMmgWR0CjN3OAqd6LdX2UKGgGaAloD0MI/kP67euwE8CUhpRSlGgVSzJoFkdAozctdcB2fXV9lChoBmgJaA9DCI50BkZelhvAlIaUUpRoFUsyaBZHQKM26R/3Fkx1fZQoaAZoCWgPQwiOdtzwu+kMwJSGlFKUaBVLMmgWR0CjOJCJoCdSdX2UKGgGaAloD0MIeNUD5iETCMCUhpRSlGgVSzJoFkdAozhK9CeEqXV9lChoBmgJaA9DCPCHn/8ePALAlIaUUpRoFUsyaBZHQKM4BOARTS91fZQoaAZoCWgPQwjwTj49tjUYwJSGlFKUaBVLMmgWR0CjN8CZF5OadX2UKGgGaAloD0MIBRbAlIHDDcCUhpRSlGgVSzJoFkdAozlmU6gdwXV9lChoBmgJaA9DCLq6Y7FNehvAlIaUUpRoFUsyaBZHQKM5IFMZgoh1fZQoaAZoCWgPQwiC4VzDDD0WwJSGlFKUaBVLMmgWR0CjONp7b+LndX2UKGgGaAloD0MIy4P0FDkUGMCUhpRSlGgVSzJoFkdAoziWOuJUHnV9lChoBmgJaA9DCOpA1lOrXxLAlIaUUpRoFUsyaBZHQKM6aM4LkS51fZQoaAZoCWgPQwjZJD/iVywGwJSGlFKUaBVLMmgWR0CjOiLl/6O6dX2UKGgGaAloD0MI0hito6pJD8CUhpRSlGgVSzJoFkdAoznc56t1ZHV9lChoBmgJaA9DCHqlLEMcqwjAlIaUUpRoFUsyaBZHQKM5mKTB68h1fZQoaAZoCWgPQwjNBplk5AwKwJSGlFKUaBVLMmgWR0CjO0Lsa86FdX2UKGgGaAloD0MIUU1J1uHIB8CUhpRSlGgVSzJoFkdAozr9Da4+bHV9lChoBmgJaA9DCDQRNjy9IhfAlIaUUpRoFUsyaBZHQKM6tvb48EF1fZQoaAZoCWgPQwiUwOYcPOMUwJSGlFKUaBVLMmgWR0CjOnKtPpIMdX2UKGgGaAloD0MIuTgqN1HLBsCUhpRSlGgVSzJoFkdAozwvsJIDo3V9lChoBmgJaA9DCDAS2nIuBQTAlIaUUpRoFUsyaBZHQKM76b+98JF1fZQoaAZoCWgPQwhQbtv3qB8IwJSGlFKUaBVLMmgWR0CjO6QQtjCpdX2UKGgGaAloD0MIu0bLgR4qHMCUhpRSlGgVSzJoFkdAoztfzH0btXV9lChoBmgJaA9DCOkPzTy5pgfAlIaUUpRoFUsyaBZHQKM9C57PY4B1fZQoaAZoCWgPQwhHjnQGRt4XwJSGlFKUaBVLMmgWR0CjPMWXb/OudX2UKGgGaAloD0MI7KNTVz77EcCUhpRSlGgVSzJoFkdAozx/lEJBxHV9lChoBmgJaA9DCBsQIa6cvRfAlIaUUpRoFUsyaBZHQKM8O1Aqur91fZQoaAZoCWgPQwg4LuOmBhoWwJSGlFKUaBVLMmgWR0CjPe6CL/CJdX2UKGgGaAloD0MItoZSexENIMCUhpRSlGgVSzJoFkdAoz2ofGMn7nV9lChoBmgJaA9DCOYeEr73BxPAlIaUUpRoFUsyaBZHQKM9Yn889wF1fZQoaAZoCWgPQwig/UgRGVYWwJSGlFKUaBVLMmgWR0CjPR4/u9eydX2UKGgGaAloD0MIDHcujPRyGcCUhpRSlGgVSzJoFkdAoz7Ra7mMfnV9lChoBmgJaA9DCIBgjh6/NwnAlIaUUpRoFUsyaBZHQKM+i2n889x1fZQoaAZoCWgPQwjIfat14pIFwJSGlFKUaBVLMmgWR0CjPkV3dKukdX2UKGgGaAloD0MIXp7OFaXkAsCUhpRSlGgVSzJoFkdAoz4BWV/tpnV9lChoBmgJaA9DCDi6SnfX+QrAlIaUUpRoFUsyaBZHQKM/ts3yZrp1fZQoaAZoCWgPQwi+Ed2zrjEQwJSGlFKUaBVLMmgWR0CjP3DlYEGJdX2UKGgGaAloD0MIa5vicVH9EsCUhpRSlGgVSzJoFkdAoz8q+lCTlnV9lChoBmgJaA9DCOfG9IQlXgLAlIaUUpRoFUsyaBZHQKM+5uXu3MJ1fZQoaAZoCWgPQwiYamYtBWQawJSGlFKUaBVLMmgWR0CjQJ8BMi8ndX2UKGgGaAloD0MILsVVZd/VE8CUhpRSlGgVSzJoFkdAo0BZDw6QvHV9lChoBmgJaA9DCNsX0At3zh7AlIaUUpRoFUsyaBZHQKNAExVyWAx1fZQoaAZoCWgPQwiHNZVFYdcFwJSGlFKUaBVLMmgWR0CjP87nX/YKdX2UKGgGaAloD0MI4NdIEoRrCcCUhpRSlGgVSzJoFkdAo0GEVk+X7nV9lChoBmgJaA9DCMKKU62FmQfAlIaUUpRoFUsyaBZHQKNBPl0YCQt1fZQoaAZoCWgPQwi6hhkaTxQYwJSGlFKUaBVLMmgWR0CjQPhCtzS1dX2UKGgGaAloD0MItoE7UKf8CMCUhpRSlGgVSzJoFkdAo0C0Uj9n9XV9lChoBmgJaA9DCCXpmsk3OwTAlIaUUpRoFUsyaBZHQKNCcZQ53kh1fZQoaAZoCWgPQwg7cM6I0g4QwJSGlFKUaBVLMmgWR0CjQiuuq3mWdX2UKGgGaAloD0MIs9KkFHQbBMCUhpRSlGgVSzJoFkdAo0HlqpLmIXV9lChoBmgJaA9DCGXEBaBRGgLAlIaUUpRoFUsyaBZHQKNBoX40uUV1fZQoaAZoCWgPQwjw3Hu45OgRwJSGlFKUaBVLMmgWR0CjQ2k12q1gdX2UKGgGaAloD0MIBfnZyHVDGsCUhpRSlGgVSzJoFkdAo0MjfLs8gnV9lChoBmgJaA9DCNieWRKgVhPAlIaUUpRoFUsyaBZHQKNC3XPJJXh1fZQoaAZoCWgPQwjIX1rUJzkPwJSGlFKUaBVLMmgWR0CjQplsHjZMdX2UKGgGaAloD0MIqODwgohUBsCUhpRSlGgVSzJoFkdAo0RfA6+36XV9lChoBmgJaA9DCKmJPh9lhA7AlIaUUpRoFUsyaBZHQKNEGSzw+dN1fZQoaAZoCWgPQwiiYTHqWssUwJSGlFKUaBVLMmgWR0CjQ9MoUi6hdX2UKGgGaAloD0MIVFOSdTi6IMCUhpRSlGgVSzJoFkdAo0OO/SH/LnV9lChoBmgJaA9DCC1A22rWoSTAlIaUUpRoFUsyaBZHQKNFTq46Oo51fZQoaAZoCWgPQwiSdqOP+YAHwJSGlFKUaBVLMmgWR0CjRQjLKV6edX2UKGgGaAloD0MI16IFaFuNDMCUhpRSlGgVSzJoFkdAo0TCyQgcLnV9lChoBmgJaA9DCENwXMZNzRLAlIaUUpRoFUsyaBZHQKNEfqQA+6l1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_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-5.15.89+-x86_64-with-debian-bullseye-sid # 1 SMP Sat Mar 11 10:24:08 UTC 2023", "Python": "3.7.12", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -4.264500357303769, "std_reward": 1.9251716145750346, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-17T09:42:35.377397"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b2e6951abfcfd17c79d73e452a5be2aaa6da70e2114a0231a52786a9c9787ff
+size 3731