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: 248.87 +/- 47.94
+      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 0x7f2db201ff70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2db2024040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2db20240d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2db2024160>", "_build": "<function ActorCriticPolicy._build at 0x7f2db20241f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f2db2024280>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2db2024310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2db20243a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f2db2024430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2db20244c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2db2024550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2db20245e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f2db2017ed0>"}, "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": 1, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676386556932955572, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAA2Thr0vRhc/pK8yPtn80b5Mhd47BIa/PAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVWRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMINBDLZg6lTUCUhpRSlIwBbJRL4IwBdJRHQKFzSFbFCLN1fZQoaAZoCWgPQwizzY3pCTs3QJSGlFKUaBVLrmgWR0Chc8B5X2dvdX2UKGgGaAloD0MIlzldFtOVcUCUhpRSlGgVTQ0BaBZHQKF0jNgSey11fZQoaAZoCWgPQwgZPbfQFWxxQJSGlFKUaBVNAAFoFkdAoXXwRChN/XV9lChoBmgJaA9DCFeyYyMQaUpAlIaUUpRoFUu7aBZHQKF2b1JUYKp1fZQoaAZoCWgPQwhS1QRR94U2QJSGlFKUaBVLyWgWR0ChdwAcT8HfdX2UKGgGaAloD0MIysFsAgxqb0CUhpRSlGgVS/xoFkdAoXe9SwW30HV9lChoBmgJaA9DCAuW6gJeCXBAlIaUUpRoFU0rAWgWR0CheJroW56MdX2UKGgGaAloD0MIpDMw8rIITkCUhpRSlGgVS+poFkdAoXnp/oaDPHV9lChoBmgJaA9DCPp6vmY56HBAlIaUUpRoFU0TAWgWR0CherxL9MsZdX2UKGgGaAloD0MILbMIxdb+bUCUhpRSlGgVS/loFkdAoXurCemNznV9lChoBmgJaA9DCOGX+nkTBHFAlIaUUpRoFUv2aBZHQKF8m+nqFAV1fZQoaAZoCWgPQwglIZG28ZhfQJSGlFKUaBVN6ANoFkdAoYNHoV2zOXV9lChoBmgJaA9DCBHfiVkvH3BAlIaUUpRoFU0RAWgWR0ChhTFolD4QdX2UKGgGaAloD0MIoz808+S0bkCUhpRSlGgVS/doFkdAoYXiaVlf7nV9lChoBmgJaA9DCDICKhzBPHFAlIaUUpRoFU0yAWgWR0ChhtfRmbsodX2UKGgGaAloD0MIwHrct1q3QkCUhpRSlGgVS9FoFkdAoYdrsUqQR3V9lChoBmgJaA9DCKaXGMv06G5AlIaUUpRoFU0iAWgWR0ChiOtYjjaPdX2UKGgGaAloD0MIejiB6bQmMUCUhpRSlGgVS8hoFkdAoYl3/WDpT3V9lChoBmgJaA9DCD4GK041M3JAlIaUUpRoFU0FAWgWR0ChijJHZsbedX2UKGgGaAloD0MIxsA6jh8BbkCUhpRSlGgVTQwBaBZHQKGLC4FzMid1fZQoaAZoCWgPQwiVDtb/Oczkv5SGlFKUaBVLp2gWR0ChjCIJqqOtdX2UKGgGaAloD0MIU1kUdlFRb0CUhpRSlGgVS/toFkdAoYzhqqOtGXV9lChoBmgJaA9DCG6JXHAGrFFAlIaUUpRoFUvAaBZHQKGNbz7uUll1fZQoaAZoCWgPQwhN2H4yRuxwQJSGlFKUaBVNMQFoFkdAoY5O+49X93V9lChoBmgJaA9DCM3K9iFvfUJAlIaUUpRoFUugaBZHQKGPYB/7SAp1fZQoaAZoCWgPQwiiDFUxlbVwQJSGlFKUaBVNSAFoFkdAoZBjcAR02nV9lChoBmgJaA9DCErrbwlA7HBAlIaUUpRoFUvyaBZHQKGRGRjBl+V1fZQoaAZoCWgPQwgrNBDLpjZwQJSGlFKUaBVL32gWR0Chkb/ReC04dX2UKGgGaAloD0MIbAa4INtBbUCUhpRSlGgVS/poFkdAoZM0/jbSJHV9lChoBmgJaA9DCCgs8YAyF29AlIaUUpRoFU0JAWgWR0ChlAPKlpGndX2UKGgGaAloD0MIvHoVGZ1zb0CUhpRSlGgVS/5oFkdAoZSx6nivPnV9lChoBmgJaA9DCGqg+Zy77TJAlIaUUpRoFUu7aBZHQKGVLzI3irF1fZQoaAZoCWgPQwj+mxcnvvFwQJSGlFKUaBVNBgFoFkdAoZXpN/OMVHV9lChoBmgJaA9DCCv52F0gT3FAlIaUUpRoFU0PAWgWR0Chl1bqIJqqdX2UKGgGaAloD0MI86rOasH0cUCUhpRSlGgVTUEBaBZHQKGYTPNVzZJ1fZQoaAZoCWgPQwgQ6iKFshxyQJSGlFKUaBVNKwFoFkdAoZl4Y+B6KXV9lChoBmgJaA9DCOHP8GaN9HFAlIaUUpRoFU0LAWgWR0Chm1o+GGmDdX2UKGgGaAloD0MIOV6B6MmPbkCUhpRSlGgVTQwBaBZHQKGcYV2Rq491fZQoaAZoCWgPQwiphZLJqQdFQJSGlFKUaBVL32gWR0ChnUxL9MsZdX2UKGgGaAloD0MIsYf2sQJucUCUhpRSlGgVTRYBaBZHQKGesWLP2PF1fZQoaAZoCWgPQwjFxVG5Cb1sQJSGlFKUaBVNHAFoFkdAoaDx+z+m33V9lChoBmgJaA9DCE+Q2O4e+HFAlIaUUpRoFU1QAWgWR0Chom2AG0NSdX2UKGgGaAloD0MIxZJy97ltb0CUhpRSlGgVS/hoFkdAoaMqguh9LHV9lChoBmgJaA9DCHqnAu55NXNAlIaUUpRoFU0VAWgWR0ChpATY287IdX2UKGgGaAloD0MIq+gPzfzacECUhpRSlGgVS/ZoFkdAoaVnfIjnm3V9lChoBmgJaA9DCA9/TdaoCHNAlIaUUpRoFU0NAWgWR0ChpjmZeAuqdX2UKGgGaAloD0MIbQGh9TAxcUCUhpRSlGgVS/toFkdAoab4Ia99MXV9lChoBmgJaA9DCE6XxcTmdzlAlIaUUpRoFUuoaBZHQKGnbOLR8dB1fZQoaAZoCWgPQwjSj4ZTZoVyQJSGlFKUaBVNHAFoFkdAoajvGp++d3V9lChoBmgJaA9DCIlFDDtMCnJAlIaUUpRoFU0wAWgWR0ChqdYcWCVbdX2UKGgGaAloD0MIGedvQuFgckCUhpRSlGgVTRUBaBZHQKGqpaM72ct1fZQoaAZoCWgPQwjwoxr2+3VyQJSGlFKUaBVNBAFoFkdAoatjExZdOnV9lChoBmgJaA9DCOC8OPHV4jxAlIaUUpRoFUvNaBZHQKGslZ5iVjZ1fZQoaAZoCWgPQwi+pDFaR0UtQJSGlFKUaBVLxmgWR0ChrSDTa0x/dX2UKGgGaAloD0MIumbyzfaqcUCUhpRSlGgVTUEBaBZHQKGuHDXOGCZ1fZQoaAZoCWgPQwhRL/g0J5RuQJSGlFKUaBVNAQFoFkdAoa7UfcN6PnV9lChoBmgJaA9DCHXHYpvUOHJAlIaUUpRoFU0sAWgWR0ChsFJ97WupdX2UKGgGaAloD0MI7ImuCz/rVECUhpRSlGgVS7poFkdAobDV1jiGWXV9lChoBmgJaA9DCDrP2JdsL3FAlIaUUpRoFU1IAWgWR0ChseaX0Gu+dX2UKGgGaAloD0MIur4PBwmQckCUhpRSlGgVS/hoFkdAobKqR0U473V9lChoBmgJaA9DCAYP0775mnJAlIaUUpRoFU1ZAWgWR0ChtF/UF0PpdX2UKGgGaAloD0MIf9+/eXEWSkCUhpRSlGgVS8ZoFkdAobTuloDgZXV9lChoBmgJaA9DCNVZLbBHy3FAlIaUUpRoFU0lAWgWR0Chtd0FB6a9dX2UKGgGaAloD0MI/+bFiS9BcECUhpRSlGgVTSABaBZHQKG3tcB2fTV1fZQoaAZoCWgPQwilEwmmmlpxQJSGlFKUaBVN/AFoFkdAobn/5BTn73V9lChoBmgJaA9DCOrPfqQI+m9AlIaUUpRoFU0aAWgWR0ChuzLU9ZA6dX2UKGgGaAloD0MI9wSJ7e7qcUCUhpRSlGgVTSUBaBZHQKG9gxUvPC51fZQoaAZoCWgPQwiPpQ9dUI9KQJSGlFKUaBVL12gWR0ChvmEX+ERKdX2UKGgGaAloD0MINNWT+UdPT0CUhpRSlGgVS85oFkdAob89vhqCYnV9lChoBmgJaA9DCHGqtTAL4G5AlIaUUpRoFU0cAWgWR0ChwHT544ZNdX2UKGgGaAloD0MIoyHjUSq1ckCUhpRSlGgVTT4BaBZHQKHCHVtGd7R1fZQoaAZoCWgPQwihvfp4KIFxQJSGlFKUaBVNCAFoFkdAocLjHKfWc3V9lChoBmgJaA9DCMnKL4MxXnFAlIaUUpRoFU0YAWgWR0Chw6zxXnyNdX2UKGgGaAloD0MII0xRLg2tbkCUhpRSlGgVS/9oFkdAocRsANoak3V9lChoBmgJaA9DCFVP5h+9X3FAlIaUUpRoFU0HAWgWR0ChxeBwuM/AdX2UKGgGaAloD0MIRPtYwW+Lb0CUhpRSlGgVTSQBaBZHQKHG2OCoS+R1fZQoaAZoCWgPQwjzkCkfgvRJQJSGlFKUaBVLtWgWR0Chx1xLkCFLdX2UKGgGaAloD0MIeo7Id+mIcECUhpRSlGgVTTABaBZHQKHIRX6InBt1fZQoaAZoCWgPQwjMs5JWfIpQQJSGlFKUaBVLrGgWR0ChyWYgq3EydX2UKGgGaAloD0MIUU1J1mGRcECUhpRSlGgVTQYBaBZHQKHKJNahYeV1fZQoaAZoCWgPQwgDste7Pz1wQJSGlFKUaBVNGgFoFkdAocr3dCVrynV9lChoBmgJaA9DCG2tLxIainFAlIaUUpRoFU0zAWgWR0Chy+s3qAz6dX2UKGgGaAloD0MIFw0Zj9Kob0CUhpRSlGgVTUEBaBZHQKHNjNATqSp1fZQoaAZoCWgPQwjqIRrdQSw3QJSGlFKUaBVLzmgWR0ChziCuuA7QdX2UKGgGaAloD0MIxvmbUAgmcECUhpRSlGgVTS4BaBZHQKHPAlsP8Q91fZQoaAZoCWgPQwg4ZtmTADlwQJSGlFKUaBVNOgNoFkdAodKRjH4oJHV9lChoBmgJaA9DCIAO8+WF2nFAlIaUUpRoFU1fAWgWR0Ch055dnkDIdX2UKGgGaAloD0MIQbrYtFLgN0CUhpRSlGgVS7RoFkdAodUAeJYT03V9lChoBmgJaA9DCI9SCU9o4G9AlIaUUpRoFU0JAWgWR0Ch1geAd4mkdX2UKGgGaAloD0MIgSbChqdccECUhpRSlGgVTRoBaBZHQKHXJgc94eN1fZQoaAZoCWgPQwhoJEIjmNpxQJSGlFKUaBVNZQFoFkdAodi/U+cH4XV9lChoBmgJaA9DCAIrhxZZanJAlIaUUpRoFU0CAWgWR0Ch2s5J04ipdX2UKGgGaAloD0MITWa8rfSOM8CUhpRSlGgVS51oFkdAodtyBqbjLnV9lChoBmgJaA9DCGxCWmMQjHFAlIaUUpRoFU0lAWgWR0Ch3M096kZadX2UKGgGaAloD0MID/Ckhcv2Q0CUhpRSlGgVS7poFkdAod2Xm3fAK3V9lChoBmgJaA9DCEwW9x+ZUm9AlIaUUpRoFU0mAWgWR0Ch31225QP7dX2UKGgGaAloD0MIh8CRQEPUcECUhpRSlGgVS/FoFkdAoeAN/lQuVXV9lChoBmgJaA9DCDCca5ghZW5AlIaUUpRoFU0JAWgWR0Ch4NcSPEKmdX2UKGgGaAloD0MITWa8rfTjcUCUhpRSlGgVTTwBaBZHQKHhzWKdhAp1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3908, "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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a7a81f10a84e762f01c93f3e802f47aa6f7f133a1c6de90f16aaf9f393bbf8c
+size 146714

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 0x7f2db201ff70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2db2024040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2db20240d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2db2024160>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f2db20241f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f2db2024280>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f2db2024310>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2db20243a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f2db2024430>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2db20244c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2db2024550>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2db20245e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f2db2017ed0>"
+    },
+    "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": 1,
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676386556932955572,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAA2Thr0vRhc/pK8yPtn80b5Mhd47BIa/PAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 3908,
+    "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 sha256:3f3b775021b4dd39db4c757d47f4fc877635e3fd9a37920d9fc72af882aa1fff
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca5f2b616e9a3539c84d997c1543454201b740d3d7417141bd31eb4fe22c3f5b
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 248.870906786626, "std_reward": 47.93945211852389, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-14T15:34:09.604880"}