First commit to the Deep RL course

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: ppo_mlp
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 243.81 +/- 16.20
+      name: mean_reward
+      verified: false
+---
+
+# **ppo_mlp** Agent playing **LunarLander-v2**
+This is a trained model of a **ppo_mlp** 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 0x7f2f8f520c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2f8f520ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2f8f520d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2f8f520dc0>", "_build": "<function ActorCriticPolicy._build at 0x7f2f8f520e50>", "forward": "<function ActorCriticPolicy.forward at 0x7f2f8f520ee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2f8f520f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2f8f523040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f2f8f5230d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2f8f523160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2f8f5231f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2f8f523280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f2f95f11c60>"}, "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": 1673709622517107148, "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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 243.81167508455943, "std_reward": 16.20259550741134, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-14T15:42:49.902352"}

smallastronaut.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3918de880678dcb56fc9dc1e2828b8d0e39201e0628a090e32aef539cab26f6c
+size 147416

smallastronaut/_stable_baselines3_version

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

smallastronaut/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 0x7f2f8f520c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2f8f520ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2f8f520d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2f8f520dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f2f8f520e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f2f8f520ee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2f8f520f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2f8f523040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f2f8f5230d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2f8f523160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2f8f5231f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2f8f523280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f2f95f11c60>"
+    },
+    "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": 1673709622517107148,
+    "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:": "gAWVexAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI6Zyf4vgPcUCUhpRSlIwBbJRNewGMAXSUR0CRgjaz/p+udX2UKGgGaAloD0MIp6/naxbocECUhpRSlGgVTWgBaBZHQJGCPJLdvbZ1fZQoaAZoCWgPQwjJIHcR5hBxQJSGlFKUaBVNfwFoFkdAkYLOFtbcGnV9lChoBmgJaA9DCMfUXdnFGHFAlIaUUpRoFU1YAWgWR0CRgzk8A7xNdX2UKGgGaAloD0MIKlJhbCFkbECUhpRSlGgVTVYBaBZHQJGFGOT7l7t1fZQoaAZoCWgPQwjWrDO+77JyQJSGlFKUaBVNJwFoFkdAkYZi5I6KcnV9lChoBmgJaA9DCLEVNC2x1EBAlIaUUpRoFUvxaBZHQJGGbHWBjF11fZQoaAZoCWgPQwiXGqGfqQNvQJSGlFKUaBVNNgFoFkdAkYiw4CIUJ3V9lChoBmgJaA9DCG9iSE6mCnJAlIaUUpRoFU02AWgWR0CRibPzFuNxdX2UKGgGaAloD0MINPJ5xdNUb0CUhpRSlGgVTSYBaBZHQJGJtFKCg9N1fZQoaAZoCWgPQwjTakjcYzVyQJSGlFKUaBVNZQFoFkdAkYog2/BWP3V9lChoBmgJaA9DCAsL7gc8qXFAlIaUUpRoFU1jAWgWR0CRi5hY/3WXdX2UKGgGaAloD0MIAfxTqkTDbkCUhpRSlGgVTaIBaBZHQJGLpHTZxrB1fZQoaAZoCWgPQwiC/kKPmJ1wQJSGlFKUaBVNFgFoFkdAkYyQuRLbpXV9lChoBmgJaA9DCGjO+pRjNXBAlIaUUpRoFU0xAWgWR0CRjLesPrfMdX2UKGgGaAloD0MIVdy4xbzCcUCUhpRSlGgVTUUBaBZHQJGM2ya/h2p1fZQoaAZoCWgPQwjcEU4LHnFxQJSGlFKUaBVNNgFoFkdAkYzsE7nxKHV9lChoBmgJaA9DCMto5PMKBnBAlIaUUpRoFU08AWgWR0CRjlULDye7dX2UKGgGaAloD0MI9FMcB177bUCUhpRSlGgVTfYBaBZHQJGO9fD1oQF1fZQoaAZoCWgPQwg095DwPehuQJSGlFKUaBVNSAFoFkdAkZDhcVxjrnV9lChoBmgJaA9DCEmgwaYOjnBAlIaUUpRoFU1DAWgWR0CRkinDiwSrdX2UKGgGaAloD0MISyNm9vn4b0CUhpRSlGgVTVkBaBZHQJGS+05U96l1fZQoaAZoCWgPQwiXOV0Wkx5xQJSGlFKUaBVNHAFoFkdAkZPguVX3g3V9lChoBmgJaA9DCNDVVuwvs25AlIaUUpRoFU01AWgWR0CRlU1cMVk+dX2UKGgGaAloD0MIxk/j3vzpb0CUhpRSlGgVTXQBaBZHQJGWj47A+IN1fZQoaAZoCWgPQwhJY7SOqmFzQJSGlFKUaBVNYwFoFkdAkZbcIVuaW3V9lChoBmgJaA9DCCdqaW4FdWxAlIaUUpRoFU02AWgWR0CRlwI42jwhdX2UKGgGaAloD0MIq8yU1l+cb0CUhpRSlGgVTUABaBZHQJGXWS5iExt1fZQoaAZoCWgPQwgR/kXQGEpwQJSGlFKUaBVNQAFoFkdAkZhME7nxKHV9lChoBmgJaA9DCDi8ICI1hGxAlIaUUpRoFU09AWgWR0CRmFa1kUbldX2UKGgGaAloD0MIPsqIC0A2bUCUhpRSlGgVTVMBaBZHQJGZOeXiR4h1fZQoaAZoCWgPQwghBORLqNBvQJSGlFKUaBVNOQFoFkdAkZpgkX1rZnV9lChoBmgJaA9DCBKEK6BQpHFAlIaUUpRoFU2KAWgWR0CRmun0kGA1dX2UKGgGaAloD0MISbw8nSvsb0CUhpRSlGgVTW0BaBZHQJGbcj2SMcZ1fZQoaAZoCWgPQwgvi4nNx204QJSGlFKUaBVL9WgWR0CRm82ECeVcdX2UKGgGaAloD0MI9yLajilKckCUhpRSlGgVTeMCaBZHQJGb3Bk7Oml1fZQoaAZoCWgPQwg2O1J952xuQJSGlFKUaBVNNgFoFkdAkZwSbhFVk3V9lChoBmgJaA9DCGXCL/VzdGxAlIaUUpRoFU0hAWgWR0CRnaePJaJRdX2UKGgGaAloD0MIZjIcz2c8RkCUhpRSlGgVS+JoFkdAkZ4DcIqsl3V9lChoBmgJaA9DCLh2oiTk6nBAlIaUUpRoFU09AWgWR0CRn+sK9f1IdX2UKGgGaAloD0MITN4AM18fakCUhpRSlGgVTaoBaBZHQJGhEHhS9/V1fZQoaAZoCWgPQwj+17lps0RvQJSGlFKUaBVNSAFoFkdAkaHNJjDsMXV9lChoBmgJaA9DCGWp9X6jDHBAlIaUUpRoFU1LAWgWR0CRomvwEyLydX2UKGgGaAloD0MILubnhib+bkCUhpRSlGgVTWIBaBZHQJGiiz3RG+d1fZQoaAZoCWgPQwi/tn76z05uQJSGlFKUaBVNRAFoFkdAkaMbsKLKm3V9lChoBmgJaA9DCBgGLLnK33BAlIaUUpRoFU1JAWgWR0CRo0/k/8l5dX2UKGgGaAloD0MIKII4DycWb0CUhpRSlGgVTVABaBZHQJGkcvpQk5Z1fZQoaAZoCWgPQwiY4NQHEoFtQJSGlFKUaBVNRQFoFkdAkbeuQdS2pnV9lChoBmgJaA9DCHV4COOn9nFAlIaUUpRoFU04AWgWR0CRt8dSl3yJdX2UKGgGaAloD0MICjGXVG17b0CUhpRSlGgVTT0BaBZHQJG4bf/FR511fZQoaAZoCWgPQwicNXhflZZtQJSGlFKUaBVNNQFoFkdAkbiNZzPrwHV9lChoBmgJaA9DCKpGrwYoRW9AlIaUUpRoFU1HAWgWR0CRuRsSkCV9dX2UKGgGaAloD0MIaMwk6gVEb0CUhpRSlGgVTToBaBZHQJG6uL9/BnB1fZQoaAZoCWgPQwjRsYNK3GlvQJSGlFKUaBVNNgFoFkdAkbr2mHgxanV9lChoBmgJaA9DCMeePZdpk3BAlIaUUpRoFU2ZAWgWR0CRu9/6O5rhdX2UKGgGaAloD0MI0o+GU+Y0cECUhpRSlGgVTScBaBZHQJG8IB6rvLJ1fZQoaAZoCWgPQwgxmL9CZoFuQJSGlFKUaBVNKwFoFkdAkb0gqd6LO3V9lChoBmgJaA9DCPWB5J1DJnJAlIaUUpRoFU0iAWgWR0CRvgfzBhx6dX2UKGgGaAloD0MIAfp9/6aZckCUhpRSlGgVTT4BaBZHQJG+ffyf+S91fZQoaAZoCWgPQwh9Bz9xwIpxQJSGlFKUaBVNLgFoFkdAkb6ZssQNC3V9lChoBmgJaA9DCCECDqHKgWtAlIaUUpRoFU0fAWgWR0CRvs4lyBCldX2UKGgGaAloD0MILQsm/iiLcECUhpRSlGgVTUcBaBZHQJG/5/SYw7F1fZQoaAZoCWgPQwh+xoUDITFyQJSGlFKUaBVNBgFoFkdAkcDQYk3S8nV9lChoBmgJaA9DCM+hDFWxonBAlIaUUpRoFU0cAWgWR0CRwNFwDNhWdX2UKGgGaAloD0MIG7gDdYpncUCUhpRSlGgVTT0BaBZHQJHA8PVd5Y51fZQoaAZoCWgPQwggCft2khZwQJSGlFKUaBVNDwFoFkdAkcEvo/zJ63V9lChoBmgJaA9DCMdmR6qvG3BAlIaUUpRoFU1LAWgWR0CRwgp++dsjdX2UKGgGaAloD0MI9s/TgIGhcUCUhpRSlGgVTSgBaBZHQJHCY+6iCat1fZQoaAZoCWgPQwjrAfOQKXdBQJSGlFKUaBVL22gWR0CRwun3+MqCdX2UKGgGaAloD0MIbO7of7kFc0CUhpRSlGgVTRABaBZHQJHELgdfb9J1fZQoaAZoCWgPQwjfG0MA8IhsQJSGlFKUaBVNSAFoFkdAkcUHrhR64XV9lChoBmgJaA9DCDUmxFyStHFAlIaUUpRoFU1XAWgWR0CRxULr5ZbIdX2UKGgGaAloD0MIJ6PKMG7Wb0CUhpRSlGgVTSIBaBZHQJHG1SJj2Bd1fZQoaAZoCWgPQwjACBozCblwQJSGlFKUaBVNRQFoFkdAkcc7Dye7MHV9lChoBmgJaA9DCF5NnrIawW5AlIaUUpRoFU0kAWgWR0CRx2fthNM5dX2UKGgGaAloD0MIM2spIO2Ya0CUhpRSlGgVTSIBaBZHQJHHsDklu3t1fZQoaAZoCWgPQwj191J40AJSQJSGlFKUaBVL9WgWR0CRyLNTLns+dX2UKGgGaAloD0MIA137AjoecECUhpRSlGgVTUkBaBZHQJHI00ZWJad1fZQoaAZoCWgPQwhTI/QztStxQJSGlFKUaBVNCgFoFkdAkcj4I0IkaHV9lChoBmgJaA9DCGVVhJvMlXFAlIaUUpRoFU03AWgWR0CRyYgssg+ydX2UKGgGaAloD0MITIxl+iUncECUhpRSlGgVTVMBaBZHQJHLfRw6ySp1fZQoaAZoCWgPQwjytPzAVepBQJSGlFKUaBVL42gWR0CRy8SVnmJWdX2UKGgGaAloD0MIzm4tk2HEbkCUhpRSlGgVTWYBaBZHQJHMBh9b5dp1fZQoaAZoCWgPQwiYaftX1r5vQJSGlFKUaBVNGgFoFkdAkcwdfb9IgHV9lChoBmgJaA9DCLuaPGU1N2xAlIaUUpRoFU1NAWgWR0CRzPd4VymzdX2UKGgGaAloD0MI4bN1cDCFcECUhpRSlGgVTXEBaBZHQJHNm0Re1KJ1fZQoaAZoCWgPQwiwPbMkwKlwQJSGlFKUaBVNOAFoFkdAkc81nyup0nV9lChoBmgJaA9DCCOgwhEky3FAlIaUUpRoFU0gAWgWR0CR0GlFc6eYdX2UKGgGaAloD0MI5MCr5Y43cECUhpRSlGgVTVQBaBZHQJHQiPS2H+J1fZQoaAZoCWgPQwgPm8jMBfdsQJSGlFKUaBVNLAFoFkdAkdEpTVDrq3V9lChoBmgJaA9DCG4UWWvolHBAlIaUUpRoFU01AWgWR0CR0eHp8neBdX2UKGgGaAloD0MIkuf6PtwsckCUhpRSlGgVTUIBaBZHQJHSEOskpqh1fZQoaAZoCWgPQwik/+VatMtuQJSGlFKUaBVNQQFoFkdAkdNrpNbkfnV9lChoBmgJaA9DCADFyJK59nBAlIaUUpRoFU0uAWgWR0CR05IxxkupdX2UKGgGaAloD0MInkMZqiINckCUhpRSlGgVTUQBaBZHQJHTs+fRNRF1fZQoaAZoCWgPQwhQyM7b2EBuQJSGlFKUaBVNZgFoFkdAkdSVfNRm9XV9lChoBmgJaA9DCEGC4scYJnBAlIaUUpRoFU0eAWgWR0CR1ZiiZfD2dX2UKGgGaAloD0MIb37DRIORcECUhpRSlGgVTTQBaBZHQJHV+7e2uxN1fZQoaAZoCWgPQwiNJEG4guxvQJSGlFKUaBVNMQFoFkdAkdYc2eg+QnV9lChoBmgJaA9DCPgb7bihknJAlIaUUpRoFU1EAWgWR0CR1joOQQtjdWUu"
+    },
+    "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
+}

smallastronaut/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58693d5831fb3dee6fe5a3c8f0a251ab887eafd129bec2e4be141a8842d3e2ec
+size 87929

smallastronaut/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c28707476ef724262f862b44451049b64a671da8ddb3b1fd7d8f77404b4c1d49
+size 43393

smallastronaut/pytorch_variables.pth

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

smallastronaut/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.0+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0