Initial 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: 277.88 +/- 19.06
+      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 0x7f23f750c430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f23f750c4c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f23f750c550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f23f750c5e0>", "_build": "<function ActorCriticPolicy._build at 0x7f23f750c670>", "forward": "<function ActorCriticPolicy.forward at 0x7f23f750c700>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f23f750c790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f23f750c820>", "_predict": "<function ActorCriticPolicy._predict at 0x7f23f750c8b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f23f750c940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f23f750c9d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f23f750ca60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f23f750ae00>"}, "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": 1680865603972018794, "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": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e4f358193b73669da284cb669f2ca7d915673655def9ff298da86a399219227
+size 147400

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7f23f750c430>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f23f750c4c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f23f750c550>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f23f750c5e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f23f750c670>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f23f750c700>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f23f750c790>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f23f750c820>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f23f750c8b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f23f750c940>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f23f750c9d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f23f750ca60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f23f750ae00>"
+    },
+    "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": 1680865603972018794,
+    "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:": "gAWVahAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMItwiM9c2UcECUhpRSlIwBbJRNAgGMAXSUR0CUOhiqhlDndX2UKGgGaAloD0MITFMEOH35cUCUhpRSlGgVTRcBaBZHQJQ6Lf51vEV1fZQoaAZoCWgPQwgVdHtJ49FsQJSGlFKUaBVL7GgWR0CUOuRrrPdEdX2UKGgGaAloD0MIMlUwKqnacECUhpRSlGgVTUUBaBZHQJQ7Vz/6wdN1fZQoaAZoCWgPQwgFptO6jVxvQJSGlFKUaBVNQgFoFkdAlDun7Lt/nXV9lChoBmgJaA9DCCTUDKniLHJAlIaUUpRoFU0SAWgWR0CUPPctXgccdX2UKGgGaAloD0MIWmJlNDIJc0CUhpRSlGgVTR0BaBZHQJQ9n4ubqhV1fZQoaAZoCWgPQwj8/s2LE+hwQJSGlFKUaBVNBgFoFkdAlD3wEdNnG3V9lChoBmgJaA9DCG8u/rantmxAlIaUUpRoFU1FAWgWR0CUPw/wRXfZdX2UKGgGaAloD0MIb9Of/YhpckCUhpRSlGgVS+xoFkdAlD8/51vETHV9lChoBmgJaA9DCNSeknOiznBAlIaUUpRoFU0vAWgWR0CUQCj5KvmpdX2UKGgGaAloD0MI+n5qvHRycUCUhpRSlGgVTSkBaBZHQJRAUBxPwd91fZQoaAZoCWgPQwi9/E6Tmb5wQJSGlFKUaBVNEAFoFkdAlECq0QbuMXV9lChoBmgJaA9DCIwUysKXrHJAlIaUUpRoFU0wAWgWR0CUQcBsANobdX2UKGgGaAloD0MIkloomVxSckCUhpRSlGgVTQwBaBZHQJRCElgMMJB1fZQoaAZoCWgPQwgiNIKN6zNyQJSGlFKUaBVNQQFoFkdAlEJNKZlWfnV9lChoBmgJaA9DCMn/5O/eQnFAlIaUUpRoFU0ZAWgWR0CUQlxiG34LdX2UKGgGaAloD0MIpnud1JcLVECUhpRSlGgVS/BoFkdAlEJmkadc0XV9lChoBmgJaA9DCNIb7iP3JXBAlIaUUpRoFU0hAWgWR0CUQ0lAeJYUdX2UKGgGaAloD0MIIlSp2cNrc0CUhpRSlGgVS+RoFkdAlEOFIAfdRHV9lChoBmgJaA9DCJW7z/FR1XJAlIaUUpRoFU0iAWgWR0CURAFJxvNvdX2UKGgGaAloD0MIfxR15h5rcECUhpRSlGgVTQgBaBZHQJRFKR+z+m51fZQoaAZoCWgPQwi5GtmVFgBzQJSGlFKUaBVNFgFoFkdAlEXfgFX7tXV9lChoBmgJaA9DCGu3XWiui19AlIaUUpRoFU3oA2gWR0CURgq7iADrdX2UKGgGaAloD0MIDY6SVyfbcUCUhpRSlGgVS/loFkdAlEYb74zrNXV9lChoBmgJaA9DCODZHr3hX3BAlIaUUpRoFU0PAWgWR0CURs2Xsw+MdX2UKGgGaAloD0MIl1gZjfwfcUCUhpRSlGgVS/doFkdAlEcYaHbh33V9lChoBmgJaA9DCI1feCVJ43FAlIaUUpRoFUvraBZHQJRIMnkT6BR1fZQoaAZoCWgPQwi6S+KsSI1wQJSGlFKUaBVNJwFoFkdAlEhIEB8x9HV9lChoBmgJaA9DCOYGQx1WQk5AlIaUUpRoFUvZaBZHQJRITgYP5Hp1fZQoaAZoCWgPQwi/u5UlOnBxQJSGlFKUaBVNBwFoFkdAlElDUy57PnV9lChoBmgJaA9DCKPMBpmk63BAlIaUUpRoFU0EAWgWR0CUSWfek56udX2UKGgGaAloD0MIkga3tQWAbUCUhpRSlGgVTQwBaBZHQJRKuJUHY6J1fZQoaAZoCWgPQwinW3aIf1ByQJSGlFKUaBVNRwFoFkdAlEuEl7dBSnV9lChoBmgJaA9DCL0A++hUEHBAlIaUUpRoFU0fAWgWR0CUS529cry2dX2UKGgGaAloD0MIwRpn09FjcUCUhpRSlGgVS/doFkdAlEwrqY7aI3V9lChoBmgJaA9DCP4mFCKgk3JAlIaUUpRoFUv0aBZHQJRM+nBLwnZ1fZQoaAZoCWgPQwg9ZTVdz8tyQJSGlFKUaBVNTwFoFkdAlE2MEV32VXV9lChoBmgJaA9DCPT91HgpIHFAlIaUUpRoFU0jAWgWR0CUTiYzBRAKdX2UKGgGaAloD0MI/S0B+GeQcUCUhpRSlGgVTQABaBZHQJROJoCdSVJ1fZQoaAZoCWgPQwiT/fM04GxwQJSGlFKUaBVNJQFoFkdAlE5alHjIaXV9lChoBmgJaA9DCOLkfocivW9AlIaUUpRoFU0IAmgWR0CUTyoCMglodX2UKGgGaAloD0MIhL2JIbnAb0CUhpRSlGgVS/1oFkdAlE9rnDBMz3V9lChoBmgJaA9DCNRkxtvKqHJAlIaUUpRoFU0pAWgWR0CUT3KhL5ARdX2UKGgGaAloD0MIbeaQ1AKecUCUhpRSlGgVTQIBaBZHQJRPl1aGHpN1fZQoaAZoCWgPQwhpOGVuPtlrQJSGlFKUaBVNFgFoFkdAlGL9V/+bVnV9lChoBmgJaA9DCJvJN9uc43JAlIaUUpRoFU0VAWgWR0CUZHrFOwgUdX2UKGgGaAloD0MIUaT7OYXhbkCUhpRSlGgVTSIBaBZHQJRlPGaQV9F1fZQoaAZoCWgPQwiZuiu7YGJvQJSGlFKUaBVNAgFoFkdAlGbbWRRuTHV9lChoBmgJaA9DCGKHMenvJW9AlIaUUpRoFU0yAWgWR0CUZ90l7dBTdX2UKGgGaAloD0MIaVTgZBsIb0CUhpRSlGgVTRoBaBZHQJRoCzUqhDh1fZQoaAZoCWgPQwg3ABsQISJyQJSGlFKUaBVNDAFoFkdAlGg3+qBEr3V9lChoBmgJaA9DCFdBDHTtRnJAlIaUUpRoFU0JAWgWR0CUaUVVghKUdX2UKGgGaAloD0MIoUliSbnOcUCUhpRSlGgVTQwBaBZHQJRqLyhBZ6l1fZQoaAZoCWgPQwj8G7RXnyduQJSGlFKUaBVNGgFoFkdAlGtWVRk3CXV9lChoBmgJaA9DCCszpfU3vGtAlIaUUpRoFU0TAWgWR0CUa1tknTiLdX2UKGgGaAloD0MIyoegavTncECUhpRSlGgVTRwBaBZHQJRrY5PuXu51fZQoaAZoCWgPQwheE9IaQ5lwQJSGlFKUaBVL92gWR0CUa+mfoRqXdX2UKGgGaAloD0MIYwlrY+wwb0CUhpRSlGgVTRYBaBZHQJRsnQw9JSR1fZQoaAZoCWgPQwjaVN0j27dyQJSGlFKUaBVNFQFoFkdAlGyd16mfoXV9lChoBmgJaA9DCLJmZJA7fXJAlIaUUpRoFU0kAWgWR0CUbLgeRxLkdX2UKGgGaAloD0MIUTOkiqKNcECUhpRSlGgVTREBaBZHQJRtBLXcxj91fZQoaAZoCWgPQwgz/KcbqBxwQJSGlFKUaBVL+mgWR0CUbVzV+Zw5dX2UKGgGaAloD0MIWaMeolHib0CUhpRSlGgVTQMBaBZHQJRt+7SRbKR1fZQoaAZoCWgPQwi5Us+CUE9xQJSGlFKUaBVNCwFoFkdAlG81i4J/onV9lChoBmgJaA9DCF6EKcqlo3NAlIaUUpRoFUv/aBZHQJRvtNj9XLh1fZQoaAZoCWgPQwhNSkG3l4hyQJSGlFKUaBVNLQFoFkdAlHDoMOPNmnV9lChoBmgJaA9DCEvnw7MEc3BAlIaUUpRoFU0CAWgWR0CUcS2hIvrXdX2UKGgGaAloD0MIwLM9ekMVcUCUhpRSlGgVTTwBaBZHQJRxPm3fAKx1fZQoaAZoCWgPQwghIcoXNFlxQJSGlFKUaBVL+GgWR0CUcdx7zCk5dX2UKGgGaAloD0MI1pC4x1LhckCUhpRSlGgVS/loFkdAlHHvk3juKHV9lChoBmgJaA9DCBIWFXF6F3FAlIaUUpRoFUvqaBZHQJRyElMRHwx1fZQoaAZoCWgPQwibr5KP3dBwQJSGlFKUaBVNAAFoFkdAlHIXWz4UOHV9lChoBmgJaA9DCNfCLLSzrnJAlIaUUpRoFU0HAWgWR0CUc229+PRzdX2UKGgGaAloD0MIIqtbPadjcECUhpRSlGgVTQQBaBZHQJRzdQ3xWkt1fZQoaAZoCWgPQwg+zF62HT9zQJSGlFKUaBVL/mgWR0CUc//pMYdidX2UKGgGaAloD0MIzEbn/JRebkCUhpRSlGgVTSEBaBZHQJR0ENOM2m51fZQoaAZoCWgPQwiu00hLZTNxQJSGlFKUaBVNDwFoFkdAlHQNVFQVK3V9lChoBmgJaA9DCJhsPNjitHFAlIaUUpRoFU2+AWgWR0CUdVEdeY2LdX2UKGgGaAloD0MIwM+4cOCVcUCUhpRSlGgVTQABaBZHQJR10yckMTh1fZQoaAZoCWgPQwg/pyA/GzVyQJSGlFKUaBVL+WgWR0CUdiMkyDZldX2UKGgGaAloD0MIXp1jQLZ+cECUhpRSlGgVTQsBaBZHQJR4FhVlwtJ1fZQoaAZoCWgPQwjY9KCglFVzQJSGlFKUaBVNDQFoFkdAlHgWRJVbRnV9lChoBmgJaA9DCG1wIvo1w3FAlIaUUpRoFUvwaBZHQJR4NPrOZ9d1fZQoaAZoCWgPQwhfXoB99HxvQJSGlFKUaBVNHwFoFkdAlHhZH/cWTHV9lChoBmgJaA9DCKacL/ZeUXBAlIaUUpRoFU0GAWgWR0CUeKxIatLddX2UKGgGaAloD0MIiZtTyUCBcUCUhpRSlGgVTSsBaBZHQJR5nhky1u11fZQoaAZoCWgPQwgi/fZ1oCpxQJSGlFKUaBVL7mgWR0CUeaaoddVvdX2UKGgGaAloD0MIYyXmWYl7cUCUhpRSlGgVTSQBaBZHQJR5pc0Ltu11fZQoaAZoCWgPQwhrup7oOm9xQJSGlFKUaBVNFgFoFkdAlHq31FpfyHV9lChoBmgJaA9DCIDVkSOdb29AlIaUUpRoFU0NAWgWR0CUeyUi6g/UdX2UKGgGaAloD0MIUrezr3zrckCUhpRSlGgVTSABaBZHQJR7lMxoIv91fZQoaAZoCWgPQwgju9Iy0l1xQJSGlFKUaBVNPQFoFkdAlHxx99c8knV9lChoBmgJaA9DCAbZsnxdom1AlIaUUpRoFUv8aBZHQJR8tg4Otnx1fZQoaAZoCWgPQwjDfk+s0/hvQJSGlFKUaBVNLQFoFkdAlH10k4WDYnV9lChoBmgJaA9DCFX5npEIgHJAlIaUUpRoFU0nAWgWR0CUfiYD1XeWdX2UKGgGaAloD0MIPs3Ji0y2bkCUhpRSlGgVS/xoFkdAlH7qLwWnCXV9lChoBmgJaA9DCGMJa2PsI25AlIaUUpRoFU0CAWgWR0CUf1dYW+GodX2UKGgGaAloD0MIXeFdLuIeckCUhpRSlGgVTS8BaBZHQJSAYX531SR1fZQoaAZoCWgPQwgsSZ7ru+JwQJSGlFKUaBVNHwFoFkdAlICAEt/WlXVlLg=="
+    },
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3621c40fd38e2d0d155f4df320395163e77cd4c01ada2821142bd7656153bc68
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c540a3559059aeba974cb114b735a801e6f3bc4358c96165ef7007148b34c2e3
+size 43393

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/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: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 277.8815302101959, "std_reward": 19.064016328674, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-07T11:40:55.458534"}