Upload model

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: 262.09 +/- 44.74
+      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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7fd25c532820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd25c5328b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd25c532940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd25c5329d0>", "_build": "<function ActorCriticPolicy._build at 0x7fd25c532a60>", "forward": "<function ActorCriticPolicy.forward at 0x7fd25c532af0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd25c532b80>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd25c532c10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd25c532ca0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd25c532d30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd25c532dc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd25c536030>"}, "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": 1670920821686940922, "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": 380, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

my_model_1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08caef7d87240336b692537bdeb54513be523c5056a90838a9453265c0fdd5b1
+size 147097

my_model_1/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

my_model_1/data

@@ -0,0 +1,94 @@
+{
+    "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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7fd25c532820>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd25c5328b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd25c532940>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd25c5329d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fd25c532a60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fd25c532af0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd25c532b80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fd25c532c10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd25c532ca0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd25c532d30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd25c532dc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fd25c536030>"
+    },
+    "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": 1670920821686940922,
+    "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:": "gAWVJhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIUFJgAUw7ckCUhpRSlIwBbJRNAQGMAXSUR0Cgq7bj94u9dX2UKGgGaAloD0MIhJohVRTxcECUhpRSlGgVS/ZoFkdAoKv1aB7NS3V9lChoBmgJaA9DCPPMy2H3MHFAlIaUUpRoFUvdaBZHQKCsAOQQtjF1fZQoaAZoCWgPQwj430p2LFNwQJSGlFKUaBVLvWgWR0CgrA1HvttzdX2UKGgGaAloD0MIiCtn74zacECUhpRSlGgVS9NoFkdAoKxSBI4EOnV9lChoBmgJaA9DCL9DUaDPqXBAlIaUUpRoFUvfaBZHQKCsuVtXPqt1fZQoaAZoCWgPQwhCeR9H8xJzQJSGlFKUaBVL0GgWR0CgrVIcBEKFdX2UKGgGaAloD0MIG7rZH6hTb0CUhpRSlGgVS7ZoFkdAoK1/Tb349HV9lChoBmgJaA9DCJzCSgUVMXJAlIaUUpRoFUvmaBZHQKCt1TiKiwl1fZQoaAZoCWgPQwgqUmFs4RVwQJSGlFKUaBVL0GgWR0CgrnWLHdXUdX2UKGgGaAloD0MIQzunWSCvcUCUhpRSlGgVS+BoFkdAoK59fu1F6XV9lChoBmgJaA9DCGthFtq55W9AlIaUUpRoFUvLaBZHQKCulguyu6p1fZQoaAZoCWgPQwgxB0FHK/BzQJSGlFKUaBVLx2gWR0Cgrp4UnG83dX2UKGgGaAloD0MIu+zXnW79YUCUhpRSlGgVTegDaBZHQKCuxujynUF1fZQoaAZoCWgPQwjL2qZ4XCRzQJSGlFKUaBVNAAFoFkdAoK7cpw0fo3V9lChoBmgJaA9DCOTYeoZwaHBAlIaUUpRoFUvPaBZHQKCu+wPAfuF1fZQoaAZoCWgPQwhsdw/QfZ1wQJSGlFKUaBVNAwFoFkdAoK7/qZ+hG3V9lChoBmgJaA9DCLAbti2KRXBAlIaUUpRoFUu7aBZHQKCvGlUIcBF1fZQoaAZoCWgPQwh2NXnKKptyQJSGlFKUaBVL8mgWR0Cgr4ED6nBMdX2UKGgGaAloD0MICRUcXpDocECUhpRSlGgVS7RoFkdAoLAlw1ivxHV9lChoBmgJaA9DCAINNnVeMHJAlIaUUpRoFUvFaBZHQKCwQJk5IYp1fZQoaAZoCWgPQwhp5V5glrxwQJSGlFKUaBVLvGgWR0CgsVlByCFsdX2UKGgGaAloD0MIkunQ6fkRc0CUhpRSlGgVS89oFkdAoLGQJgLJCHV9lChoBmgJaA9DCASsVbummHFAlIaUUpRoFUv1aBZHQKCxkHrQgLZ1fZQoaAZoCWgPQwiIaHQHsY1tQJSGlFKUaBVLv2gWR0CgseSr5qM4dX2UKGgGaAloD0MIMGXggJa0cECUhpRSlGgVS91oFkdAoLHw/9pAU3V9lChoBmgJaA9DCKpjldJzO3FAlIaUUpRoFUvlaBZHQKCx9ZpztC11fZQoaAZoCWgPQwi4kh0bwYdwQJSGlFKUaBVL1mgWR0CgskB60IC2dX2UKGgGaAloD0MIEodsIB0dckCUhpRSlGgVS+xoFkdAoLJZ7AtWdXV9lChoBmgJaA9DCBLZB1kWtm1AlIaUUpRoFUu4aBZHQKCyYac7Qsx1fZQoaAZoCWgPQwjByqFF9hFyQJSGlFKUaBVL62gWR0Cgsm0zCUHIdX2UKGgGaAloD0MIdnEbDeBocUCUhpRSlGgVS+JoFkdAoLKPGn4wiHV9lChoBmgJaA9DCP7w89+DYnBAlIaUUpRoFUvSaBZHQKCzea4tpVV1fZQoaAZoCWgPQwhslPWbiaxyQJSGlFKUaBVL6GgWR0Cgs+4Oc2BKdX2UKGgGaAloD0MIZXJqZ1i4ckCUhpRSlGgVS9loFkdAoLTgBJZntnV9lChoBmgJaA9DCFN7EW2HVHBAlIaUUpRoFUvSaBZHQKC1Vn8Kohp1fZQoaAZoCWgPQwilEp7Q61ltQJSGlFKUaBVLtWgWR0CgtVvFvQ4TdX2UKGgGaAloD0MIe4hGdxDVckCUhpRSlGgVS+loFkdAoLVmBg/kenV9lChoBmgJaA9DCBXHgVdLSHBAlIaUUpRoFUvBaBZHQKC1h/Yrauh1fZQoaAZoCWgPQwiCOXr8XjJxQJSGlFKUaBVL3mgWR0CgtZlDneSCdX2UKGgGaAloD0MI31FjQkxEcUCUhpRSlGgVS/doFkdAoLWi6BiCrnV9lChoBmgJaA9DCMb83NCUd2tAlIaUUpRoFUvDaBZHQKC1zhWo3rF1fZQoaAZoCWgPQwi/02TG29pyQJSGlFKUaBVL0mgWR0CgteSNOuaGdX2UKGgGaAloD0MIJTyh198ncUCUhpRSlGgVS+VoFkdAoLYC06YE4nV9lChoBmgJaA9DCNPbn4sGa3JAlIaUUpRoFUu8aBZHQKC2osGPgel1fZQoaAZoCWgPQwg7/DVZI29xQJSGlFKUaBVLvGgWR0CgtxwyyleodX2UKGgGaAloD0MIiQtAozQHcUCUhpRSlGgVS7hoFkdAoLh5LCemN3V9lChoBmgJaA9DCANbJVgcmG5AlIaUUpRoFUu/aBZHQKC4lqHoHLR1fZQoaAZoCWgPQwjgEoB/ykpyQJSGlFKUaBVL3mgWR0CguLTw2ETQdX2UKGgGaAloD0MIRKfn3RiqcUCUhpRSlGgVS+JoFkdAoLl1uJk5InV9lChoBmgJaA9DCKA3Fakwr3JAlIaUUpRoFUvVaBZHQKC5iwKSgXd1fZQoaAZoCWgPQwgAqrhxC4RxQJSGlFKUaBVL5GgWR0CguZPJA+pwdX2UKGgGaAloD0MI6iEa3YHNcECUhpRSlGgVS/BoFkdAoLmWCGvfTHV9lChoBmgJaA9DCNPAj2pYpnFAlIaUUpRoFUvpaBZHQKC5tSVGCqZ1fZQoaAZoCWgPQwh1OpD1VPVwQJSGlFKUaBVL62gWR0CguiPsZ5zHdX2UKGgGaAloD0MIbVM8LurIcUCUhpRSlGgVS/xoFkdAoLpIoPTXrnV9lChoBmgJaA9DCEjdzr5yRm1AlIaUUpRoFUvUaBZHQKC64UPhAGB1fZQoaAZoCWgPQwjAIypUt+JyQJSGlFKUaBVNAgFoFkdAoLsuKXOW0XV9lChoBmgJaA9DCGtlwi91/2RAlIaUUpRoFU3oA2gWR0Cgu3qujh1ldX2UKGgGaAloD0MIsktUbw2yZECUhpRSlGgVTegDaBZHQKC8FFERaox1fZQoaAZoCWgPQwgcCTTY1MVuQJSGlFKUaBVLzGgWR0CgvCCRwIdEdX2UKGgGaAloD0MIdv7tsh/5cECUhpRSlGgVS+NoFkdAoLxiylenh3V9lChoBmgJaA9DCI8c6QwMQXFAlIaUUpRoFUu4aBZHQKC8bpCa7Vd1fZQoaAZoCWgPQwhQcRx4NWtxQJSGlFKUaBVL62gWR0CgvGu/tY0VdX2UKGgGaAloD0MI+Z/83Tu2cECUhpRSlGgVS75oFkdAoLy5wMpgC3V9lChoBmgJaA9DCNu/stKkpmVAlIaUUpRoFU3oA2gWR0CgvOFnAZbZdX2UKGgGaAloD0MIPPazWAqOc0CUhpRSlGgVS9VoFkdAoLzx5qubJHV9lChoBmgJaA9DCK685H+yfXBAlIaUUpRoFUvcaBZHQKC8/0rbxmV1fZQoaAZoCWgPQwi2uwfoflhyQJSGlFKUaBVL72gWR0CgvUAQ6IWQdX2UKGgGaAloD0MI6gQ0EbZQcECUhpRSlGgVS9doFkdAoL17lvIfbXV9lChoBmgJaA9DCIZwzLLnj3JAlIaUUpRoFUv2aBZHQKC9vWQwK0F1fZQoaAZoCWgPQwjV6qurwrdxQJSGlFKUaBVL12gWR0Cgvi+Q2dd3dX2UKGgGaAloD0MIasAg6ROEcUCUhpRSlGgVS/hoFkdAoL5l1dPcjHV9lChoBmgJaA9DCOiiIeNR1nBAlIaUUpRoFUu6aBZHQKC+oID5j6N1fZQoaAZoCWgPQwjLLhhcc2pxQJSGlFKUaBVL7GgWR0Cgvr0fozN2dX2UKGgGaAloD0MIj1AzpMruc0CUhpRSlGgVS8doFkdAoL8Jw84ginV9lChoBmgJaA9DCDVEFf6MP25AlIaUUpRoFUvLaBZHQKC/Hc1wYLt1fZQoaAZoCWgPQwhkrgyqDStyQJSGlFKUaBVL1mgWR0Cgv0ZEMLF5dX2UKGgGaAloD0MIkUQvo9iicECUhpRSlGgVS/ZoFkdAoL9kq6OHWXV9lChoBmgJaA9DCJvo81GGG3JAlIaUUpRoFUvOaBZHQKC/bH7P6bh1fZQoaAZoCWgPQwiB0eXNIVJwQJSGlFKUaBVLy2gWR0Cgv4SjpLVXdX2UKGgGaAloD0MIfv578NpCcUCUhpRSlGgVS85oFkdAoL+dzMibD3V9lChoBmgJaA9DCG5t4XmpEm9AlIaUUpRoFUvBaBZHQKC/w7UXpGF1fZQoaAZoCWgPQwjdIjDWdzdxQJSGlFKUaBVL5WgWR0Cgv+4QJ5VwdX2UKGgGaAloD0MI8wLsoxNrckCUhpRSlGgVS/9oFkdAoMDF+TeO43V9lChoBmgJaA9DCKzKvisCdGtAlIaUUpRoFUvHaBZHQKDAwieNDMN1fZQoaAZoCWgPQwgUsB2MmMxwQJSGlFKUaBVL1WgWR0CgwSmOMl1KdX2UKGgGaAloD0MI6IcRwqM+ZECUhpRSlGgVTegDaBZHQKDBUQK8cuJ1fZQoaAZoCWgPQwj6mA8ItHNwQJSGlFKUaBVLyWgWR0CgwVnf/FR6dX2UKGgGaAloD0MIN3Fyv8PZb0CUhpRSlGgVS7loFkdAoMGBo4+8oXV9lChoBmgJaA9DCBWt3AuMIXJAlIaUUpRoFUvraBZHQKDBr4etCAt1fZQoaAZoCWgPQwiI1/ULdoVyQJSGlFKUaBVLyGgWR0Cgwdxyn1nNdX2UKGgGaAloD0MI7na9NEUkcUCUhpRSlGgVS9poFkdAoMHdLOAy23V9lChoBmgJaA9DCLNCke7nMnJAlIaUUpRoFUvSaBZHQKDCGanaWX11fZQoaAZoCWgPQwg9ZMqHIMtwQJSGlFKUaBVL2WgWR0Cgwjd6Tnq3dX2UKGgGaAloD0MIsDvdeeIycUCUhpRSlGgVS+RoFkdAoMJ2JcgQpXV9lChoBmgJaA9DCF0ZVBucwnBAlIaUUpRoFUvTaBZHQKDCg6wMYuV1fZQoaAZoCWgPQwhcyvlib+5tQJSGlFKUaBVL72gWR0CgwrjVx0dSdX2UKGgGaAloD0MIL/zgfCoYckCUhpRSlGgVS9doFkdAoMLFVtGd7XV9lChoBmgJaA9DCGa+g5+4KXBAlIaUUpRoFUvTaBZHQKDDlh1DBuZ1ZS4="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 380,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

my_model_1/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34d7b8f857b0302725a04faf2aaa95ff967c5f0a6659469ebba4ba818488ba7a
+size 87929

my_model_1/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56853a438c39d76edb80b2ca7a8b1954ef76ee9d6ddb024f747136ab9cc81a42
+size 43201

my_model_1/pytorch_variables.pth

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

my_model_1/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 262.0886119377484, "std_reward": 44.738910870638435, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-13T09:28:05.337853"}