first commit!!

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 267.77 +/- 14.19
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-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
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    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\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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7f7530819550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f75308195e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7530819670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7530819700>", "_build": "<function ActorCriticPolicy._build at 0x7f7530819790>", "forward": "<function ActorCriticPolicy.forward at 0x7f7530819820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f75308198b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7530819940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f75308199d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7530819a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7530819af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7530819b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f75308221c0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679932729807311378, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "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": 267.7725437818168, "std_reward": 14.19110898063561, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T16:24:55.517034"}

unit1_ppo.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a683e7b0c496911164678c6207ec0e253c455a1a4fbcc8d7356b2b1654766e5
+size 147409

unit1_ppo/_stable_baselines3_version

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

unit1_ppo/data

@@ -0,0 +1,95 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    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\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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7f7530819550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f75308195e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7530819670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7530819700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7530819790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f7530819820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f75308198b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7530819940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f75308199d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7530819a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7530819af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7530819b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f75308221c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679932729807311378,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVcBAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI3EjZImmMYUCUhpRSlIwBbJRN6AOMAXSUR0CVSaZP2wmmdX2UKGgGaAloD0MIa4E9JlIeXECUhpRSlGgVTegDaBZHQJVJ8f2bobJ1fZQoaAZoCWgPQwjMejGUEzBkQJSGlFKUaBVN6ANoFkdAlUwgzP8htHV9lChoBmgJaA9DCMk7hzJUIWRAlIaUUpRoFU3oA2gWR0CVTaAWzniedX2UKGgGaAloD0MIEHo2q74RYkCUhpRSlGgVTegDaBZHQJVORBMSK3x1fZQoaAZoCWgPQwhFEVK3MxBjQJSGlFKUaBVN6ANoFkdAlU5UtAcDKnV9lChoBmgJaA9DCNTzbiwoA2ZAlIaUUpRoFU3oA2gWR0CVUxf7rLQpdX2UKGgGaAloD0MIHH433bJHYECUhpRSlGgVTegDaBZHQJVVqKgqVhV1fZQoaAZoCWgPQwg18Q7wpHdSQJSGlFKUaBVNBwFoFkdAlVZUoBq9G3V9lChoBmgJaA9DCPGdmPViIWNAlIaUUpRoFU3oA2gWR0CVVnBY3eendX2UKGgGaAloD0MIvYxiuSV+YkCUhpRSlGgVTegDaBZHQJVWd9F4LTh1fZQoaAZoCWgPQwiatKm6R3xlQJSGlFKUaBVN6ANoFkdAlV8acd5prXV9lChoBmgJaA9DCF/QQgJG/UZAlIaUUpRoFUvpaBZHQJVg8jmjj711fZQoaAZoCWgPQwj43XTLjklgQJSGlFKUaBVN6ANoFkdAlWuJAyEcsHV9lChoBmgJaA9DCChJ10w+uWFAlIaUUpRoFU3oA2gWR0CVeasQd0aIdX2UKGgGaAloD0MITYbj+QyYX0CUhpRSlGgVTegDaBZHQJWRXFGXokl1fZQoaAZoCWgPQwgzjSYX41NlQJSGlFKUaBVN6ANoFkdAlZHcTviLl3V9lChoBmgJaA9DCLBwkuYPSmFAlIaUUpRoFU3oA2gWR0CVklS5RTCMdX2UKGgGaAloD0MI5wEs8msaYUCUhpRSlGgVTegDaBZHQJWWMxREWqN1fZQoaAZoCWgPQwgfniXIiBBmQJSGlFKUaBVN6ANoFkdAlZiwjyFwk3V9lChoBmgJaA9DCArys5Fr22FAlIaUUpRoFU3oA2gWR0CVmhokzGgjdX2UKGgGaAloD0MIpwaazzlmYUCUhpRSlGgVTegDaBZHQJWapqmCROl1fZQoaAZoCWgPQwhruTMTjNxjQJSGlFKUaBVN6ANoFkdAlZq0a6z3RHV9lChoBmgJaA9DCI4fKo2Yd0BAlIaUUpRoFUvtaBZHQJWcnhIe5nV1fZQoaAZoCWgPQwjlK4GU2JpkQJSGlFKUaBVN6ANoFkdAlZ8h4QjD9HV9lChoBmgJaA9DCMVx4NVys2VAlIaUUpRoFU3oA2gWR0CVoksBhhH9dX2UKGgGaAloD0MIsB2M2CcIZkCUhpRSlGgVTegDaBZHQJWiaFVT72t1fZQoaAZoCWgPQwiWy0bnfGpmQJSGlFKUaBVN6ANoFkdAlaJvxQSBb3V9lChoBmgJaA9DCIIf1bDflWZAlIaUUpRoFU3oA2gWR0CVrijI7vG7dX2UKGgGaAloD0MIcy7FVWVcY0CUhpRSlGgVTegDaBZHQJWxEbiqABl1fZQoaAZoCWgPQwhC0qdV9PRhQJSGlFKUaBVN6ANoFkdAlb218kUsWnV9lChoBmgJaA9DCOc0C7Q7YExAlIaUUpRoFUvNaBZHQJW+lf7aZhN1fZQoaAZoCWgPQwj8HB8tDshwQJSGlFKUaBVN9gJoFkdAlcUc3AEdNnV9lChoBmgJaA9DCFzjM9k/rHBAlIaUUpRoFU2qAmgWR0CVxeqzJIUbdX2UKGgGaAloD0MIAi7IluXaYkCUhpRSlGgVTegDaBZHQJXHJksjFAF1fZQoaAZoCWgPQwgwLH++LbpNQJSGlFKUaBVL6mgWR0CV3dFqBVdYdX2UKGgGaAloD0MIbm5MT1iCZ0CUhpRSlGgVTegDaBZHQJXd7MjeKsN1fZQoaAZoCWgPQwiUh4VaU15oQJSGlFKUaBVN6ANoFkdAld5YHs1KoXV9lChoBmgJaA9DCHtpigCnd9O/lIaUUpRoFUv+aBZHQJXf3R3NcGF1fZQoaAZoCWgPQwiwIM1YtNBhQJSGlFKUaBVN6ANoFkdAleMa3VkMC3V9lChoBmgJaA9DCPj7xWzJKkVAlIaUUpRoFUv0aBZHQJXmQ5zYEnt1fZQoaAZoCWgPQwhvLv62JyhMQJSGlFKUaBVL82gWR0CV5yIC2c8UdX2UKGgGaAloD0MIqn06HrNSYUCUhpRSlGgVTegDaBZHQJXn/BLwnYx1fZQoaAZoCWgPQwhjDRe5J05lQJSGlFKUaBVN6ANoFkdAlei+scQyynV9lChoBmgJaA9DCCk+PiE7cWNAlIaUUpRoFU3oA2gWR0CV6NLMLWqcdX2UKGgGaAloD0MIecn/5G9gYUCUhpRSlGgVTegDaBZHQJXuvixVyWB1fZQoaAZoCWgPQwidZRah2OoZwJSGlFKUaBVNAAFoFkdAle7eIdlunHV9lChoBmgJaA9DCM77/zhhciVAlIaUUpRoFUv2aBZHQJXxCa7VawF1fZQoaAZoCWgPQwgOwAZEiL1AQJSGlFKUaBVL0WgWR0CV8ZjXFtKqdX2UKGgGaAloD0MIO4pz1FG5Y0CUhpRSlGgVTegDaBZHQJXx7ENvwVl1fZQoaAZoCWgPQwhLdQEvM+dkQJSGlFKUaBVN6ANoFkdAlfIF67dzn3V9lChoBmgJaA9DCKhTHt2Iu2NAlIaUUpRoFU3oA2gWR0CV8gsLfDUFdX2UKGgGaAloD0MITQ8KStE6GECUhpRSlGgVS9VoFkdAlfJEfgaWHHV9lChoBmgJaA9DCH7H8NhP+GNAlIaUUpRoFU3oA2gWR0CV+WnCwbEQdX2UKGgGaAloD0MIi21S0VjRR0CUhpRSlGgVS9xoFkdAlfttRNyo43V9lChoBmgJaA9DCBr6J7hYmS1AlIaUUpRoFUvzaBZHQJX8mreZXuF1fZQoaAZoCWgPQwifO8H+62JwQJSGlFKUaBVN+QFoFkdAlgFy2Yv38HV9lChoBmgJaA9DCGbAWUqW5UJAlIaUUpRoFUvpaBZHQJYKiBWgezV1fZQoaAZoCWgPQwikOEcdHeFmQJSGlFKUaBVN6ANoFkdAlhCkfs/puHV9lChoBmgJaA9DCJhr0QK0M2RAlIaUUpRoFU3oA2gWR0CWLK2VVxS6dX2UKGgGaAloD0MIpUxqaINaYkCUhpRSlGgVTegDaBZHQJYsyXHBDXx1fZQoaAZoCWgPQwjEz38P3iZiQJSGlFKUaBVN6ANoFkdAli1SfYjB23V9lChoBmgJaA9DCPnzbcFSxWRAlIaUUpRoFU3oA2gWR0CWNk/ffoA5dX2UKGgGaAloD0MIjXvzGyahZUCUhpRSlGgVTegDaBZHQJY3CUr08Nh1fZQoaAZoCWgPQwh5P26//MBhQJSGlFKUaBVN6ANoFkdAlj1E8NhE0HV9lChoBmgJaA9DCJQxPsxeGWFAlIaUUpRoFU3oA2gWR0CWPWTr3TNMdX2UKGgGaAloD0MI9UnusIk4XUCUhpRSlGgVTegDaBZHQJY/x1fVqet1fZQoaAZoCWgPQwjIDFTGP2tiQJSGlFKUaBVN6ANoFkdAlkCdnoPkJnV9lChoBmgJaA9DCDGXVG23p2ZAlIaUUpRoFU3oA2gWR0CWQQbyYoiLdX2UKGgGaAloD0MIMSWS6GWDZUCUhpRSlGgVTegDaBZHQJZBei8Fpwl1fZQoaAZoCWgPQwi8dmnDYV1NQJSGlFKUaBVL0mgWR0CWQoz5XU6QdX2UKGgGaAloD0MIAFRx4xaTSECUhpRSlGgVS+9oFkdAlkjgieNDMXV9lChoBmgJaA9DCGMq/YQzmWBAlIaUUpRoFU3oA2gWR0CWSZJ6IFeOdX2UKGgGaAloD0MItk3xuKg4XkCUhpRSlGgVTegDaBZHQJZLW2JBPbh1fZQoaAZoCWgPQwinlq31RWBKQJSGlFKUaBVL8mgWR0CWTL8Nx2jgdX2UKGgGaAloD0MIxjGSPcI5YkCUhpRSlGgVTegDaBZHQJZQzBDXvph1fZQoaAZoCWgPQwh5knTN5F9kQJSGlFKUaBVN6ANoFkdAllwdutOmBXV9lChoBmgJaA9DCPxx++WTamFAlIaUUpRoFU3oA2gWR0CWY8qPfbbldX2UKGgGaAloD0MIHzF6buHRcUCUhpRSlGgVTaACaBZHQJZlZSydFv11fZQoaAZoCWgPQwj6X65Fi/RhQJSGlFKUaBVN6ANoFkdAlmdd6C17Y3V9lChoBmgJaA9DCGdjJeZZI2ZAlIaUUpRoFU3oA2gWR0CWZ3r3Cbc5dX2UKGgGaAloD0MILUDbalZMY0CUhpRSlGgVTegDaBZHQJZn8psoDxN1fZQoaAZoCWgPQwi5qBYRRdlkQJSGlFKUaBVN6ANoFkdAloMgnQY1pHV9lChoBmgJaA9DCKWEYFW9QGNAlIaUUpRoFU3oA2gWR0CWjFUXYUWVdX2UKGgGaAloD0MITu0MU1vaZUCUhpRSlGgVTegDaBZHQJaNil9Brvd1fZQoaAZoCWgPQwjYKOs3k99iQJSGlFKUaBVN6ANoFkdAlo4epS75EnV9lChoBmgJaA9DCPa0w18Tl2dAlIaUUpRoFU3oA2gWR0CWkH9Aood/dX2UKGgGaAloD0MI8YKI1LRhZkCUhpRSlGgVTegDaBZHQJaaNuGbkOt1fZQoaAZoCWgPQwit30xMl4BxQJSGlFKUaBVNUQNoFkdAlprbYwqRU3V9lChoBmgJaA9DCP9eCg8aE2ZAlIaUUpRoFU3oA2gWR0CWmy1UEPlNdX2UKGgGaAloD0MIFr6+1qXSZUCUhpRSlGgVTegDaBZHQJadOvkili11fZQoaAZoCWgPQwh/wAMDiGtiQJSGlFKUaBVN6ANoFkdAlp6QNwzch3V9lChoBmgJaA9DCHKIuDmVRk1AlIaUUpRoFUveaBZHQJaoevGIbfh1fZQoaAZoCWgPQwjOjlTfebhjQJSGlFKUaBVN6ANoFkdAlqixuO0b+HV9lChoBmgJaA9DCFQB9zx/QEVAlIaUUpRoFU0hAWgWR0CWql5iVjZtdX2UKGgGaAloD0MIh2wgXWycYUCUhpRSlGgVTegDaBZHQJatkIUrTYx1fZQoaAZoCWgPQwhQOSaLe+1oQJSGlFKUaBVN6ANoFkdAlq7Li++M63V9lChoBmgJaA9DCC+ISE272GZAlIaUUpRoFU3oA2gWR0CWsHSn+AEudX2UKGgGaAloD0MIg/qWOd0vY0CUhpRSlGgVTegDaBZHQJawkFhXr+p1fZQoaAZoCWgPQwhOm3EaIitjQJSGlFKUaBVN6ANoFkdAlrDxwuM+/3VlLg=="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

unit1_ppo/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:264d7312e3a214cf729243d0a8aa2c71d9e1ffb1935d921947de7fe901039eed
+size 87929

unit1_ppo/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d29912582d7729b07f061accc5bc2d0362911a6871cd390850985b13982427f0
+size 43393

unit1_ppo/pytorch_variables.pth

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

unit1_ppo/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