Upload PPO LunarLander-v2 trained agent

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: 292.83 +/- 17.61
+      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 0x7fbc8cba2310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbc8cba23a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbc8cba2430>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbc8cba24c0>", "_build": "<function ActorCriticPolicy._build at 0x7fbc8cba2550>", "forward": "<function ActorCriticPolicy.forward at 0x7fbc8cba25e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbc8cba2670>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbc8cba2700>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbc8cba2790>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbc8cba2820>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbc8cba28b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbc8cba2940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fbc8cba1d00>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-1.5       -1.5       -5.        -5.        -3.1415927 -5.\n -0.        -0.       ]", "high": "[1.5       1.5       5.        5.        3.1415927 5.        1.\n 1.       ]", "low_repr": "[-1.5       -1.5       -5.        -5.        -3.1415927 -5.\n -0.        -0.       ]", "high_repr": "[1.5       1.5       5.        5.        3.1415927 5.        1.\n 1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "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", "n": 4, "start": 0, "_shape": [], "dtype": "int64", "_np_random": "RandomState(MT19937)"}, "n_envs": 16, "num_timesteps": 51233600, "_total_timesteps": 80000000, "_num_timesteps_at_start": 0, "seed": 344042492, "action_noise": null, "start_time": 1671398835470193282, "learning_rate": 0.0, "tensorboard_log": "tensorboard-logs/ppo-LunarLander-v2-stable-reward-penalize-time/LunarLander-v2", "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": 0.3595904, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 12508, "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"}}

ppo1-meow-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8988d556259bb7ccf3007cf0d75872bc8b0c5914bea99cba49c6edb4f5168e8a
+size 151404

ppo1-meow-LunarLander-v2/_stable_baselines3_version

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

ppo1-meow-LunarLander-v2/data

@@ -0,0 +1,98 @@
+{
+    "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 0x7fbc8cba2310>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbc8cba23a0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbc8cba2430>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbc8cba24c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbc8cba2550>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbc8cba25e0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbc8cba2670>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbc8cba2700>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbc8cba2790>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbc8cba2820>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbc8cba28b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbc8cba2940>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fbc8cba1d00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-1.5       -1.5       -5.        -5.        -3.1415927 -5.\n -0.        -0.       ]",
+        "high": "[1.5       1.5       5.        5.        3.1415927 5.        1.\n 1.       ]",
+        "low_repr": "[-1.5       -1.5       -5.        -5.        -3.1415927 -5.\n -0.        -0.       ]",
+        "high_repr": "[1.5       1.5       5.        5.        3.1415927 5.        1.\n 1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "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",
+        "n": 4,
+        "start": 0,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "RandomState(MT19937)"
+    },
+    "n_envs": 16,
+    "num_timesteps": 51233600,
+    "_total_timesteps": 80000000,
+    "_num_timesteps_at_start": 0,
+    "seed": 344042492,
+    "action_noise": null,
+    "start_time": 1671398835470193282,
+    "learning_rate": 0.0,
+    "tensorboard_log": "tensorboard-logs/ppo-LunarLander-v2-stable-reward-penalize-time/LunarLander-v2",
+    "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": 0.3595904,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIU7MHWgFnbECUhpRSlIwBbJRLgIwBdJRHQNwtXQDmr811fZQoaAZoCWgPQwhLzLOSlqNwQJSGlFKUaBVLtGgWR0DcLV0I7eVLdX2UKGgGaAloD0MIWOGWj6Rua0CUhpRSlGgVS45oFkdA3C1dSE12q3V9lChoBmgJaA9DCOYHrvJEOHBAlIaUUpRoFUupaBZHQNwtX74nF5x1fZQoaAZoCWgPQwi2+BQAY/BvQJSGlFKUaBVLrmgWR0DcLWSfra/RdX2UKGgGaAloD0MI2uTwSacmbkCUhpRSlGgVS4hoFkdA3C1oLpRoAXV9lChoBmgJaA9DCGTll8EY3W5AlIaUUpRoFUufaBZHQNwtZ6wljVh1fZQoaAZoCWgPQwjs+3CQEA5rQJSGlFKUaBVLkGgWR0DcLXBMJx//dX2UKGgGaAloD0MII57sZka6b0CUhpRSlGgVS3poFkdA3C1wsDW9UXV9lChoBmgJaA9DCPxvJTs2K25AlIaUUpRoFUumaBZHQNwtcM1TBIp1fZQoaAZoCWgPQwir61BNyZprQJSGlFKUaBVLhWgWR0DcLXGTSsr/dX2UKGgGaAloD0MI8WjjiLXRbkCUhpRSlGgVS6VoFkdA3C1yUJv5xnV9lChoBmgJaA9DCDvj++LS4G5AlIaUUpRoFUuwaBZHQNwtcxs/IKd1fZQoaAZoCWgPQwhmLQWkfaBwQJSGlFKUaBVLkmgWR0DcLXNyksSTdX2UKGgGaAloD0MIJv29FN4XcUCUhpRSlGgVS6RoFkdA3C1zDst03nV9lChoBmgJaA9DCJVh3A0i7GxAlIaUUpRoFUugaBZHQNwtd3hfjS51fZQoaAZoCWgPQwi4VnvYi+JtQJSGlFKUaBVLkWgWR0DcLXl6gM+edX2UKGgGaAloD0MIgNb8+AvrcECUhpRSlGgVS5toFkdA3C19eK8+R3V9lChoBmgJaA9DCEDBxYraJ3BAlIaUUpRoFUutaBZHQNwtfek1uR91fZQoaAZoCWgPQwiyn8VSJLNwQJSGlFKUaBVLrWgWR0DcLX3OryUcdX2UKGgGaAloD0MIJGJKJFEDcECUhpRSlGgVS4xoFkdA3C2DIDYAbXV9lChoBmgJaA9DCL1UbMzr821AlIaUUpRoFUuhaBZHQNwthLOZ9eB1fZQoaAZoCWgPQwj3OT5aHAJwQJSGlFKUaBVLlmgWR0DcLYWKqGUOdX2UKGgGaAloD0MIs193uvOUakCUhpRSlGgVS4loFkdA3C2J3MY/FHV9lChoBmgJaA9DCLmpgeZzGHBAlIaUUpRoFUuHaBZHQNwtiieiBXl1fZQoaAZoCWgPQwhhbYyd8H1tQJSGlFKUaBVLkmgWR0DcLYs67ulXdX2UKGgGaAloD0MI547+l+s+b0CUhpRSlGgVS6toFkdA3C2O1c+qznV9lChoBmgJaA9DCHv2XKYmhGtAlIaUUpRoFUuDaBZHQNwtjqJ66at1fZQoaAZoCWgPQwjedTbkH+hwQJSGlFKUaBVLm2gWR0DcLY8zLwF1dX2UKGgGaAloD0MIRDAOLh1/bECUhpRSlGgVS6BoFkdA3C2QLV4HHHV9lChoBmgJaA9DCGed8X1xxW1AlIaUUpRoFUulaBZHQNwtkERODap1fZQoaAZoCWgPQwgVONkG7ihuQJSGlFKUaBVLpmgWR0DcLZCOU+s6dX2UKGgGaAloD0MIrFYm/NISbkCUhpRSlGgVS5BoFkdA3C2StAs053V9lChoBmgJaA9DCHdNSGsMg21AlIaUUpRoFUucaBZHQNwtmPbGm1p1fZQoaAZoCWgPQwjog2Vs6A9xQJSGlFKUaBVLo2gWR0DcLZqL3sX0dX2UKGgGaAloD0MIX1yq0hbGb0CUhpRSlGgVS7BoFkdA3C2dVSXMQnV9lChoBmgJaA9DCBL1gk/znm1AlIaUUpRoFUueaBZHQNwtoN1EE1V1fZQoaAZoCWgPQwgdyeU/pCdvQJSGlFKUaBVLrWgWR0DcLaEgkka/dX2UKGgGaAloD0MIBtodUoxQb0CUhpRSlGgVS61oFkdA3C2ioAXEZXV9lChoBmgJaA9DCJTBUfJqi2xAlIaUUpRoFUuYaBZHQNwtpbGipNt1fZQoaAZoCWgPQwi+a9CXHrNwQJSGlFKUaBVLpWgWR0DcLaaMUAT7dX2UKGgGaAloD0MIxLDDmHRebECUhpRSlGgVS35oFkdA3C2mL5h0AHV9lChoBmgJaA9DCCbGMv2SwmtAlIaUUpRoFUuIaBZHQNwtpztoi9t1fZQoaAZoCWgPQwgsu2BwzclvQJSGlFKUaBVLemgWR0DcLajbUPQOdX2UKGgGaAloD0MII9qOqfsscECUhpRSlGgVS7BoFkdA3C2qEJ0GNnV9lChoBmgJaA9DCCDsFKsGHW5AlIaUUpRoFUuZaBZHQNwtqr3TNMZ1fZQoaAZoCWgPQwjfiO5Z10xwQJSGlFKUaBVLrWgWR0DcLa+1iONpdX2UKGgGaAloD0MIUYNpGL7bbUCUhpRSlGgVS6doFkdA3C2wUGmk33V9lChoBmgJaA9DCCoAxjNohW9AlIaUUpRoFUuiaBZHQNwtsFgH/tJ1fZQoaAZoCWgPQwiMKy6OSgZvQJSGlFKUaBVLi2gWR0DcLbo8uBczdX2UKGgGaAloD0MIiljEsMNybECUhpRSlGgVS6BoFkdA3C26DzRQanV9lChoBmgJaA9DCCnOUUdHSm9AlIaUUpRoFUuJaBZHQNwtvqLKmsN1fZQoaAZoCWgPQwiVY7K4fzBtQJSGlFKUaBVLg2gWR0DcLb3f779AdX2UKGgGaAloD0MICahwBCkVbUCUhpRSlGgVS69oFkdA3C2+vLowEnV9lChoBmgJaA9DCBNDcjJxUW9AlIaUUpRoFUuEaBZHQNwtw4VRDTl1fZQoaAZoCWgPQwgYBcHj2xhvQJSGlFKUaBVLsGgWR0DcLcfwWnCPdX2UKGgGaAloD0MI5iK+E/NpcECUhpRSlGgVS6RoFkdA3C3I+MZP23V9lChoBmgJaA9DCIrJG2Bm5XBAlIaUUpRoFUueaBZHQNwtyNYwIt11fZQoaAZoCWgPQwg4E9OF2LVpQJSGlFKUaBVLk2gWR0DcLclWEK3NdX2UKGgGaAloD0MIdxTnqCMCcUCUhpRSlGgVS5JoFkdA3C3JmUW2w3V9lChoBmgJaA9DCLMMcayLW21AlIaUUpRoFUuCaBZHQNwtywe7tiR1fZQoaAZoCWgPQwguVtRgGvtwQJSGlFKUaBVLpGgWR0DcLcnlQuVYdX2UKGgGaAloD0MIa2KBr+jKbkCUhpRSlGgVS45oFkdA3C3MGZuyeXV9lChoBmgJaA9DCBL3WPpQDm1AlIaUUpRoFUutaBZHQNwtzKHGjsV1fZQoaAZoCWgPQwjGwaVjTstwQJSGlFKUaBVLomgWR0DcLc/HYHxCdX2UKGgGaAloD0MI2exI9Z3ybECUhpRSlGgVS4doFkdA3C3XRfnfVXV9lChoBmgJaA9DCCsyOiCJImpAlIaUUpRoFUucaBZHQNwt1tvwVj91fZQoaAZoCWgPQwhLWYY41kptQJSGlFKUaBVLoGgWR0DcLdfKhcqwdX2UKGgGaAloD0MI8rImFnjEbUCUhpRSlGgVS5toFkdA3C3a1J17pnV9lChoBmgJaA9DCAHBHD0+p3BAlIaUUpRoFUuIaBZHQNwt3EyP+4t1fZQoaAZoCWgPQwguyQG7msFwQJSGlFKUaBVLtGgWR0DcLd5Esrd4dX2UKGgGaAloD0MI8Q2Fz1bub0CUhpRSlGgVS39oFkdA3C3fk+otMHV9lChoBmgJaA9DCGSw4lTr6mxAlIaUUpRoFUuCaBZHQNwt4GapgkV1fZQoaAZoCWgPQwj0a+unfy1sQJSGlFKUaBVLimgWR0DcLeGlCTlldX2UKGgGaAloD0MIqwZhbnd5a0CUhpRSlGgVS51oFkdA3C3j0elsQHV9lChoBmgJaA9DCAgAjj37o25AlIaUUpRoFUuKaBZHQNwt5M2eg+R1fZQoaAZoCWgPQwjikXh5OpZuQJSGlFKUaBVLp2gWR0DcLeZZX+2mdX2UKGgGaAloD0MIeuOkMO/GbkCUhpRSlGgVS6BoFkdA3C3nMYuTR3V9lChoBmgJaA9DCEGC4scYI21AlIaUUpRoFUuiaBZHQNwt6Y7zTWp1fZQoaAZoCWgPQwgAkBMmzERxQJSGlFKUaBVLwmgWR0DcLevA/LTydX2UKGgGaAloD0MINum2RK7ZbkCUhpRSlGgVS6xoFkdA3C3ucnE2pHV9lChoBmgJaA9DCN/eNehLAWtAlIaUUpRoFUuOaBZHQNwt8CHuZ1F1fZQoaAZoCWgPQwgi+rX1k9JwQJSGlFKUaBVLjmgWR0DcLe+04R29dX2UKGgGaAloD0MI2QdZFkzWb0CUhpRSlGgVS5BoFkdA3C3w5CF9KHV9lChoBmgJaA9DCNLHfECgaG1AlIaUUpRoFUuZaBZHQNwt9Z7PY4B1fZQoaAZoCWgPQwi2LjVCP5BvQJSGlFKUaBVLp2gWR0DcLfaFQEZBdX2UKGgGaAloD0MIvFtZonMycECUhpRSlGgVS5JoFkdA3C33YiPhh3V9lChoBmgJaA9DCJXXSuguvW9AlIaUUpRoFUulaBZHQNwt+cuvllt1fZQoaAZoCWgPQwh9JZASO6xrQJSGlFKUaBVLmmgWR0DcLfnSjQAudX2UKGgGaAloD0MI3/3xXjVicECUhpRSlGgVS6xoFkdA3C3+SElE7XV9lChoBmgJaA9DCIAsRIeAdHBAlIaUUpRoFUt6aBZHQNwt/wG8mKJ1fZQoaAZoCWgPQwge+YOBp2JwQJSGlFKUaBVLqmgWR0DcLgA0ygwodX2UKGgGaAloD0MIr13acNj5b0CUhpRSlGgVS4RoFkdA3C3+ll9SdnV9lChoBmgJaA9DCFa6u86GlG1AlIaUUpRoFUudaBZHQNwuAJ1/2Cd1fZQoaAZoCWgPQwjAP6VKlOdtQJSGlFKUaBVLoGgWR0DcLf/A0sOHdX2UKGgGaAloD0MIvOtsyL9/cECUhpRSlGgVS7BoFkdA3C4EDPnjhnV9lChoBmgJaA9DCDfdskN8kW5AlIaUUpRoFUt+aBZHQNwuBC2hIvt1fZQoaAZoCWgPQwiJ0XMLXW9uQJSGlFKUaBVLm2gWR0DcLgixqwhXdX2UKGgGaAloD0MIT5FDxA2RcECUhpRSlGgVS6loFkdA3C862fkFOnV9lChoBmgJaA9DCMu8VdehYnBAlIaUUpRoFUu0aBZHQNwvPSUX5311ZS4="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 12508,
+    "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:": "gAWVwwEAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUtDQwRkAVMAlE5HAAAAAAAAAACGlCmMAV+UhZSMHjxpcHl0aG9uLWlucHV0LTYtNmZlNTkzMTI0MDZhPpSMCDxsYW1iZGE+lEsNQwCUKSl0lFKUfZQojAtfX3BhY2thZ2VfX5ROjAhfX25hbWVfX5SMCF9fbWFpbl9flHVOTk50lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaBZ9lH2UKGgTaA2MDF9fcXVhbG5hbWVfX5RoDYwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoFIwHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5ROjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo1-meow-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e208284190c3429b84372e92bb5e6b49f5307c5fcd17a4d6ffdd82963267c0b
+size 88057

ppo1-meow-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfc948d4644559f1d78bb052f311f43e3b6f2b7c7ee9574682d8b37f55ae896f
+size 43393

ppo1-meow-LunarLander-v2/pytorch_variables.pth

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

ppo1-meow-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: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 292.83054526655053, "std_reward": 17.60860844150231, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-18T20:28:36.649186"}