Upload PPO LunarLander-v2 trained agent

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 241.58 +/- 14.87
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+  # **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
+  

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 0x7ff1211c3440>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff1211c34d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff1211c3560>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff1211c35f0>", "_build": "<function ActorCriticPolicy._build at 0x7ff1211c3680>", "forward": "<function ActorCriticPolicy.forward at 0x7ff1211c3710>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff1211c37a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff1211c3830>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff1211c38c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff1211c3950>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff1211c39e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff12120ca20>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651861962.275069, "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:": "gAWVbxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIK27cYn4TXECUhpRSlIwBbJRN6AOMAXSUR0CIuGSWZ7XydX2UKGgGaAloD0MIjnbc8LtxW0CUhpRSlGgVTegDaBZHQIjKFaUzKtB1fZQoaAZoCWgPQwiAfXTqyqNbQJSGlFKUaBVN6ANoFkdAiM5xPfsNUnV9lChoBmgJaA9DCBGq1OwBj2BAlIaUUpRoFU3oA2gWR0CJBQkKNQ0odX2UKGgGaAloD0MIvVXXoZqSvL+UhpRSlGgVS85oFkdAiQfooE0SAnV9lChoBmgJaA9DCMh9q3Vi82FAlIaUUpRoFU3oA2gWR0CJFS4EwFkhdX2UKGgGaAloD0MIbF7VWS1pYUCUhpRSlGgVTegDaBZHQIkds3AEdNp1fZQoaAZoCWgPQwgaGk8E8QZiQJSGlFKUaBVN6ANoFkdAiR8zot+TeXV9lChoBmgJaA9DCC4e3nNgaFdAlIaUUpRoFU3oA2gWR0CJJo0TlDF7dX2UKGgGaAloD0MIcxHfidklZ0CUhpRSlGgVTS8CaBZHQIknHXumaYx1fZQoaAZoCWgPQwiwAny3+ednQJSGlFKUaBVNlQJoFkdAiSdw6QvHtHV9lChoBmgJaA9DCF37Anph8GVAlIaUUpRoFU3oA2gWR0CJOEmR/3FldX2UKGgGaAloD0MILo81IwM5YkCUhpRSlGgVTegDaBZHQIk9Iq7ROUN1fZQoaAZoCWgPQwjVIw1ua+1AQJSGlFKUaBVL7GgWR0CJR16JqIrOdX2UKGgGaAloD0MI4Ep2bAQYQkCUhpRSlGgVS+xoFkdAiUhDmr8zh3V9lChoBmgJaA9DCNbG2AmvwmJAlIaUUpRoFU3oA2gWR0CJT+mjTKDDdX2UKGgGaAloD0MIpG38iUpGY0CUhpRSlGgVTegDaBZHQIlSmSKWLP51fZQoaAZoCWgPQwjW/s726LteQJSGlFKUaBVN6ANoFkdAiVQtE5Qxe3V9lChoBmgJaA9DCJ/L1CR4aUNAlIaUUpRoFUv1aBZHQIleUvwmVqx1fZQoaAZoCWgPQwilZg+0AqBjQJSGlFKUaBVN6ANoFkdAiXLSCOFQEnV9lChoBmgJaA9DCIYeMXruxmFAlIaUUpRoFU3oA2gWR0CJiUiblRxcdX2UKGgGaAloD0MIq1s9J73TXUCUhpRSlGgVTegDaBZHQImOOMhouf51fZQoaAZoCWgPQwgcXhCRmlY/QJSGlFKUaBVN6ANoFkdAiZEltj0+T3V9lChoBmgJaA9DCEZ8J2a9X15AlIaUUpRoFU3oA2gWR0CJyMKeCkGidX2UKGgGaAloD0MIXTelvFbQS0CUhpRSlGgVTegDaBZHQInWfeDWbw11fZQoaAZoCWgPQwg9murJ/LdbQJSGlFKUaBVN6ANoFkdAid75mRNh3XV9lChoBmgJaA9DCMv3jETod2NAlIaUUpRoFU3oA2gWR0CJ4G/UvwmWdX2UKGgGaAloD0MIHqfoSK5MYECUhpRSlGgVTegDaBZHQInoF0Rvm5l1fZQoaAZoCWgPQwh7EW3H1A5fQJSGlFKUaBVN6ANoFkdAifpKgAZKnXV9lChoBmgJaA9DCDICKhxBMV5AlIaUUpRoFU3oA2gWR0CKCzZmI0qIdX2UKGgGaAloD0MIIJxPHav4X0CUhpRSlGgVTegDaBZHQIoMRPj4pMJ1fZQoaAZoCWgPQwi3lzRG6xRdQJSGlFKUaBVN6ANoFkdAihSe0gKWs3V9lChoBmgJaA9DCGh5Htyd51tAlIaUUpRoFU3oA2gWR0CKF2TOgQHzdX2UKGgGaAloD0MIcQFolK5KYECUhpRSlGgVTegDaBZHQIoZOICU5dZ1fZQoaAZoCWgPQwi/mC1ZFSFbQJSGlFKUaBVN6ANoFkdAiiRZD7ZWaXV9lChoBmgJaA9DCMJoVrYPZV5AlIaUUpRoFU3oA2gWR0CKOOoxYaHcdX2UKGgGaAloD0MI3Qn2X+fhXECUhpRSlGgVTegDaBZHQIpPNzdUKiR1fZQoaAZoCWgPQwha12g50ENhQJSGlFKUaBVN6ANoFkdAilQp+c6Nl3V9lChoBmgJaA9DCHMSSl+IUWNAlIaUUpRoFU3oA2gWR0CKVzSsKb8WdX2UKGgGaAloD0MIUwPN59zMX0CUhpRSlGgVTegDaBZHQIpabOs1baB1fZQoaAZoCWgPQwgg1bDfE+9kQJSGlFKUaBVN6ANoFkdAip1DvVmSQ3V9lChoBmgJaA9DCPuQt1z99V1AlIaUUpRoFU3oA2gWR0CKpiOFxn3+dX2UKGgGaAloD0MIexSuR2FXZECUhpRSlGgVTegDaBZHQIqnsth/iHZ1fZQoaAZoCWgPQwg3pbxWQgFjQJSGlFKUaBVN6ANoFkdAiq9oNEw353V9lChoBmgJaA9DCOcAwRw9PjdAlIaUUpRoFUvsaBZHQIqwECRwIdF1fZQoaAZoCWgPQwjGiEShZb0MQJSGlFKUaBVLt2gWR0CKwJ3mmtQsdX2UKGgGaAloD0MIODKP/EG8YECUhpRSlGgVTegDaBZHQIrA4qNIbwV1fZQoaAZoCWgPQwhKYkm5+w9hQJSGlFKUaBVN6ANoFkdAis+91dPcjHV9lChoBmgJaA9DCHnqkQY3jGRAlIaUUpRoFU3oA2gWR0CK0L1EmY0EdX2UKGgGaAloD0MIo4/5gMA5ZECUhpRSlGgVTegDaBZHQIrYC5sj3VV1fZQoaAZoCWgPQwhrYoGvaGdgQJSGlFKUaBVN6ANoFkdAitqc2zfJm3V9lChoBmgJaA9DCDnwarkzr1lAlIaUUpRoFU3oA2gWR0CK3CXGff4zdX2UKGgGaAloD0MIsK4K1GK0M0CUhpRSlGgVS+JoFkdAit9lGoaUA3V9lChoBmgJaA9DCDGx+bi29WJAlIaUUpRoFU3oA2gWR0CK5ZnZkCmudX2UKGgGaAloD0MI6gWf5uS5N0CUhpRSlGgVS+JoFkdAivLxCx/us3V9lChoBmgJaA9DCP1oOGVuWGFAlIaUUpRoFU3oA2gWR0CK90mG/N7jdX2UKGgGaAloD0MIM3BAS1dQF0CUhpRSlGgVS+1oFkdAivhkE1VHWnV9lChoBmgJaA9DCJZ4QNmUUFxAlIaUUpRoFU3oA2gWR0CLChtv4ubrdX2UKGgGaAloD0MI2e4eoPvHXUCUhpRSlGgVTegDaBZHQIsOUVgx8D11fZQoaAZoCWgPQwjEsS5uo/BfQJSGlFKUaBVN6ANoFkdAixDId2gWanV9lChoBmgJaA9DCN8xPPYzZmFAlIaUUpRoFU3oA2gWR0CLVTTx5LRKdX2UKGgGaAloD0MIaOc0CzTwYkCUhpRSlGgVTegDaBZHQItd/Xyy2QZ1fZQoaAZoCWgPQwgnE7cKYlw7QJSGlFKUaBVL3WgWR0CLYxmp2ll9dX2UKGgGaAloD0MIBmUaTS7HXUCUhpRSlGgVTegDaBZHQItnu6y0KJF1fZQoaAZoCWgPQwiHFtnO9yFcQJSGlFKUaBVN6ANoFkdAi2hLlV94NnV9lChoBmgJaA9DCKOs30xMMz1AlIaUUpRoFUvDaBZHQItvml2vB8B1fZQoaAZoCWgPQwhIpkOn5/NbQJSGlFKUaBVN6ANoFkdAi3c4+B6KL3V9lChoBmgJaA9DCGSvd3+8dURAlIaUUpRoFUvIaBZHQIuA0pqh11Z1fZQoaAZoCWgPQwhzZrtCH7VgQJSGlFKUaBVN6ANoFkdAi4VIC+10DHV9lChoBmgJaA9DCDl+qDTirGBAlIaUUpRoFU3oA2gWR0CLhh+l0o0AdX2UKGgGaAloD0MIP8iyYOIrRUCUhpRSlGgVS8loFkdAi4nm2LHdXXV9lChoBmgJaA9DCCMva2KBVWNAlIaUUpRoFU3oA2gWR0CLj/qpLmITdX2UKGgGaAloD0MIUvF/R1SnYUCUhpRSlGgVTegDaBZHQIuVcefZmI11fZQoaAZoCWgPQwhETIkk+rRiQJSGlFKUaBVN6ANoFkdAi5wrC3w1BXV9lChoBmgJaA9DCJV87C5Q2kVAlIaUUpRoFUvUaBZHQIuh0fJV81J1fZQoaAZoCWgPQwiyEB0CRxJgQJSGlFKUaBVN6ANoFkdAi6ryWZ7Xx3V9lChoBmgJaA9DCMhhMH+FyVhAlIaUUpRoFU3oA2gWR0CLr3DXvphXdX2UKGgGaAloD0MIeT4D6k1aZECUhpRSlGgVTegDaBZHQIuwif6Ggzx1fZQoaAZoCWgPQwhubHak+oRhQJSGlFKUaBVN6ANoFkdAi8KfN7jT8nV9lChoBmgJaA9DCJjD7juGg2FAlIaUUpRoFU3oA2gWR0CLxu33pOerdX2UKGgGaAloD0MIv/G1Z5ZNYUCUhpRSlGgVTegDaBZHQIwYXGQ0XP91fZQoaAZoCWgPQwg+JlKazVtgQJSGlFKUaBVN6ANoFkdAjB2oxYaHbnV9lChoBmgJaA9DCGiR7Xw/SV9AlIaUUpRoFU3oA2gWR0CMIvYV6/qPdX2UKGgGaAloD0MIteGwNHBPZECUhpRSlGgVTegDaBZHQIw0QtSQ5m11fZQoaAZoCWgPQwhT6Sec3f1ZQJSGlFKUaBVN6ANoFkdAjD9APuogm3V9lChoBmgJaA9DCAn5oGezs11AlIaUUpRoFU3oA2gWR0CMQ/q4YrJ9dX2UKGgGaAloD0MIgEdUqG5FXECUhpRSlGgVTegDaBZHQIxIw0oBq9J1fZQoaAZoCWgPQwhhwf2AhzdiQJSGlFKUaBVN6ANoFkdAjE79jPOY6XV9lChoBmgJaA9DCNZwkXs6OWNAlIaUUpRoFU3oA2gWR0CMVJk8zQ/pdX2UKGgGaAloD0MILH5TWKmFXECUhpRSlGgVTegDaBZHQIxbloJzDGd1fZQoaAZoCWgPQwj6nLtdL5xdQJSGlFKUaBVN6ANoFkdAjGFX1anrIHV9lChoBmgJaA9DCGFu93KfzF9AlIaUUpRoFU3oA2gWR0CMajpbD/EPdX2UKGgGaAloD0MIEoPAyqFhNECUhpRSlGgVS8VoFkdAjG5vpyIYWXV9lChoBmgJaA9DCOS6KeW1D19AlIaUUpRoFU3oA2gWR0CMbrfLLZBcdX2UKGgGaAloD0MIR1oqb0etXkCUhpRSlGgVTegDaBZHQIxv3fGdZq51fZQoaAZoCWgPQwgE4nX9gg0ZQJSGlFKUaBVL6GgWR0CMfrK1XvH+dX2UKGgGaAloD0MIysFsAgwRYkCUhpRSlGgVTegDaBZHQIyAccABDG91fZQoaAZoCWgPQwiOA6+WOzleQJSGlFKUaBVN6ANoFkdAjIQvv0AcUHV9lChoBmgJaA9DCHB6F+/H/SJAlIaUUpRoFUvYaBZHQIyFB1DBuXN1fZQoaAZoCWgPQwjAdjBin5AuQJSGlFKUaBVL3mgWR0CMiubm2b5NdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 124, "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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bf21914078c328737fb8801bae4863af0cdab3e39f71d38e078158d9dbf11a60
+size 233493

results.json

@@ -0,0 +1 @@
+{"mean_reward": 241.57595204933614, "std_reward": 14.865129194261716, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T18:54:13.650173"}

test-ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f08b29df2675844aea3b668edc2120b67ea993bdf099e1d6604122d0201d81e
+size 144023

test-ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

test-ppo-LunarLander-v2/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 0x7ff1211c3440>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff1211c34d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff1211c3560>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff1211c35f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff1211c3680>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff1211c3710>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff1211c37a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff1211c3830>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff1211c38c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff1211c3950>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff1211c39e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7ff12120ca20>"
+    },
+    "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": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1651861962.275069,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAALO8nT5HHku9lbmgPBU1jLtU/KG+xQBlNQAAgD8AAAAA+vA4vtxLRrwS5oe8HFGwuqyDrT1uuo87AACAPwAAgD8iQOa+rzVGP+l3nz32UZy+eOAuvb7H1D0AAAAAAAAAAL70nL7srm0+gU2rPXXNlr5+AzS9R4mNvQAAAAAAAAAAUMp6vkPMOT3e8nm6rEfmOFZ51L7ySAe4AAAAAAAAgD8Afgw9H4XbtWG7hjpmBY40vZ/pu2ExobkAAIA/AACAP2AwEr4pzlA79vGVO19IgrmWkOu82AFoOgAAgD8AAIA/s0oGvkjXhjt4S0Q8Z7bFupUrJb3j5LI7AACAPwAAgD/2AHK+fHFYPxUi3r1S3ba+gxo+vm2BfT4AAAAAAAAAADOv0j32jEC6krqxutGVpLWCSbC5uzDOOQAAgD8AAIA/4NNIvhykArxilmc78ToeOVtNjj3Kioy6AACAPwAAgD82ka0+t9nsPt5ko73j46G+1a+HPVYRgjwAAAAAAAAAAEYNHj5xA1i7x+SOuptBmDd326W8o/WlOQAAgD8AAIA/GsSCvm7x4LwlZWC71HmOuY+YTT76hYE6AACAPwAAgD/A24c+qRWsPmOBeTzB2oq+6krmPfWPfLwAAAAAAAAAAMAaBD6uv4S6vvapO16wPLYuwXW71LnGugAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_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": 124,
+    "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
+}

test-ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:75cf48db6bfa6a35479a705bfb4cea25daab22be126bfe9783168a8b8b07fac3
+size 84829

test-ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:152a75ba61560c961dd8fa74f4e271cafaaafd4f618f9ca237af341de211de78
+size 43201

test-ppo-LunarLander-v2/pytorch_variables.pth

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

test-ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0