RL Course, Lesson 1

.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: 224.46 +/- 14.33
+      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 0x7f83b758b050>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f83b758b0e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f83b758b170>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f83b758b200>", "_build": "<function ActorCriticPolicy._build at 0x7f83b758b290>", "forward": "<function ActorCriticPolicy.forward at 0x7f83b758b320>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f83b758b3b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f83b758b440>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f83b758b4d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f83b758b560>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f83b758b5f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f83b75d85d0>"}, "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": 524288, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652029988.0280137, "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": -0.04857599999999995, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 160, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a3a0d6475e0dd027297fc231731f83f65be7e7c3fb7e3c34180b3ec048e2a169
+size 144094

ppo-LunarLander-v2/_stable_baselines3_version

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

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 0x7f83b758b050>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f83b758b0e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f83b758b170>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f83b758b200>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f83b758b290>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f83b758b320>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f83b758b3b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f83b758b440>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f83b758b4d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f83b758b560>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f83b758b5f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f83b75d85d0>"
+    },
+    "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": 524288,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652029988.0280137,
+    "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": -0.04857599999999995,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVdBAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIEw1S8BRQcECUhpRSlIwBbJRNKwKMAXSUR0CVPOOCXhOydX2UKGgGaAloD0MI7KS+LG3YY0CUhpRSlGgVTegDaBZHQJU9g/HHWBl1fZQoaAZoCWgPQwhA3UCBd8IeQJSGlFKUaBVLzGgWR0CVPnjgQ6IWdX2UKGgGaAloD0MIdcx5xj7hbUCUhpRSlGgVTTADaBZHQJVF8npjc211fZQoaAZoCWgPQwjedTbkH2hmQJSGlFKUaBVN6ANoFkdAlU1Wf029+XV9lChoBmgJaA9DCKz9ne1RFHFAlIaUUpRoFU00A2gWR0CVT7lnAZbZdX2UKGgGaAloD0MI7Z+nAYOnakCUhpRSlGgVTV0CaBZHQJVUZlxwQ191fZQoaAZoCWgPQwjImSZsP2tcQJSGlFKUaBVN6ANoFkdAlVSTvE0iyXV9lChoBmgJaA9DCBcrajANs2xAlIaUUpRoFU2YAWgWR0CVWxbI91U3dX2UKGgGaAloD0MIeH+8Vy3abkCUhpRSlGgVTe8BaBZHQJViRIre67N1fZQoaAZoCWgPQwhsCmR2Fv1iQJSGlFKUaBVN6ANoFkdAlWM0v0yxiXV9lChoBmgJaA9DCC+ISE27FWBAlIaUUpRoFU3oA2gWR0CVZlCnP3SKdX2UKGgGaAloD0MI2AxwQbZ/cUCUhpRSlGgVTSMCaBZHQJVnEPYnOSp1fZQoaAZoCWgPQwhy/iYUIgJjQJSGlFKUaBVN6ANoFkdAlWucQyylenV9lChoBmgJaA9DCPBt+rOf4WNAlIaUUpRoFU3oA2gWR0CVbUbgjyFxdX2UKGgGaAloD0MINPj7xey8b0CUhpRSlGgVTV0DaBZHQJVtdzMibDx1fZQoaAZoCWgPQwhCXDl752tgQJSGlFKUaBVN6ANoFkdAlW2H7Hhjv3V9lChoBmgJaA9DCDLJyFnY7FRAlIaUUpRoFU3oA2gWR0CVcfpWFN+LdX2UKGgGaAloD0MI/mDgufeKVkCUhpRSlGgVTegDaBZHQJWBDkHUtqZ1fZQoaAZoCWgPQwjEtG/ur4Y6QJSGlFKUaBVNCgFoFkdAlYQx/mT1TXV9lChoBmgJaA9DCEERixh2ymJAlIaUUpRoFU3oA2gWR0CVjJJa7mMgdX2UKGgGaAloD0MIUWnEzL51bkCUhpRSlGgVTfEBaBZHQJWO8/Y8Md91fZQoaAZoCWgPQwg43EduzX9tQJSGlFKUaBVNJAJoFkdAlZBw8SwnpnV9lChoBmgJaA9DCB/4GKw40WFAlIaUUpRoFU3oA2gWR0CVk0s6JZW8dX2UKGgGaAloD0MI73N8tDh6X0CUhpRSlGgVTegDaBZHQJWVTLOiWVx1fZQoaAZoCWgPQwiZZyWteJpvQJSGlFKUaBVNWAJoFkdAlZWB/ZuhsnV9lChoBmgJaA9DCNY3MLlRUl9AlIaUUpRoFU3oA2gWR0CVmM1mJ3xGdX2UKGgGaAloD0MIZ4F2h5QUY0CUhpRSlGgVTegDaBZHQJWY70AcT8J1fZQoaAZoCWgPQwjR5ji3yRFyQJSGlFKUaBVN3ANoFkdAlZ0vQv6CUXV9lChoBmgJaA9DCNs1Ia0x2CdAlIaUUpRoFUvZaBZHQJWdg5fdAPd1fZQoaAZoCWgPQwgX1/hM9n8aQJSGlFKUaBVL0mgWR0CVn4jjJdSmdX2UKGgGaAloD0MI91j60AUUZECUhpRSlGgVTegDaBZHQJWjWV9nbqR1fZQoaAZoCWgPQwjRdkzdFUNjQJSGlFKUaBVN6ANoFkdAlaQVHavicXV9lChoBmgJaA9DCAKBzqRN32FAlIaUUpRoFU3oA2gWR0CVpqNEgGKRdX2UKGgGaAloD0MIaCWt+IYuOkCUhpRSlGgVS/NoFkdAlabTAaef7XV9lChoBmgJaA9DCKM6Hch6jV9AlIaUUpRoFU3oA2gWR0CVp0n/T9bYdX2UKGgGaAloD0MIRx0dVyPzNkCUhpRSlGgVS+BoFkdAlax0mx+rl3V9lChoBmgJaA9DCAbVBieiWmJAlIaUUpRoFU3oA2gWR0CVrKQhwEQodX2UKGgGaAloD0MISzrKwWy+MMCUhpRSlGgVS/doFkdAlbLEAksz23V9lChoBmgJaA9DCEMc6+K2WGdAlIaUUpRoFU0UAmgWR0CVtJ0IkZ75dX2UKGgGaAloD0MIeO+oMaGab0CUhpRSlGgVTT8DaBZHQJW32WiUPhB1fZQoaAZoCWgPQwi1M0xtqb1sQJSGlFKUaBVNBQJoFkdAlkYX+ZPVNHV9lChoBmgJaA9DCHx+GCE8mW9AlIaUUpRoFU0BAmgWR0CWRjrlvIfbdX2UKGgGaAloD0MIBCDu6lXoMcCUhpRSlGgVTRABaBZHQJZHlavA44p1fZQoaAZoCWgPQwgEkUWaePBhQJSGlFKUaBVN6ANoFkdAlklq/VRUFXV9lChoBmgJaA9DCDLohNCBrnBAlIaUUpRoFU1XAWgWR0CWS8wc5sCUdX2UKGgGaAloD0MI1Jy8yATNbkCUhpRSlGgVTcwCaBZHQJZNgKVpsXV1fZQoaAZoCWgPQwjyQ6URsyduQJSGlFKUaBVN/QFoFkdAlk+SDyvs7nV9lChoBmgJaA9DCOqvV1jwDW9AlIaUUpRoFU3DA2gWR0CWUOTMqz7edX2UKGgGaAloD0MIOq3boPadQUCUhpRSlGgVS71oFkdAllLWOp84P3V9lChoBmgJaA9DCOf9f5ywSGJAlIaUUpRoFU3oA2gWR0CWVP7O3UhFdX2UKGgGaAloD0MI44kgzkMTbkCUhpRSlGgVTWcBaBZHQJZXGQ+2Vml1fZQoaAZoCWgPQwjhuIybGnNvQJSGlFKUaBVNnQFoFkdAlldUbYK6WnV9lChoBmgJaA9DCFK69C9Jsl9AlIaUUpRoFU3oA2gWR0CWWntDD0lJdX2UKGgGaAloD0MID7VtGAU7cECUhpRSlGgVTT4BaBZHQJZbwPCl7+l1fZQoaAZoCWgPQwgsnnqkgZpwQJSGlFKUaBVNNAFoFkdAll1odhiLEXV9lChoBmgJaA9DCKeRlspbpHBAlIaUUpRoFU2SAWgWR0CWXakKeCkHdX2UKGgGaAloD0MITmA6rdvyQECUhpRSlGgVS9hoFkdAlmE7RSgoPXV9lChoBmgJaA9DCCgs8YAyD3FAlIaUUpRoFU00A2gWR0CWYWcZ9/jLdX2UKGgGaAloD0MIPdS2YRQeaECUhpRSlGgVTXcDaBZHQJZl49s7+1l1fZQoaAZoCWgPQwhUbqKW5k5cQJSGlFKUaBVN6ANoFkdAlmjHaFmFrXV9lChoBmgJaA9DCJ9yTBb3qzRAlIaUUpRoFUvGaBZHQJZpL7BO58V1fZQoaAZoCWgPQwhkB5W4DpRuQJSGlFKUaBVNPAFoFkdAlmlylJpWWHV9lChoBmgJaA9DCIBkOnT6fGxAlIaUUpRoFU25AWgWR0CWammHxjJ/dX2UKGgGaAloD0MIUyKJXkZ/cECUhpRSlGgVTQoBaBZHQJZrVf0Eov11fZQoaAZoCWgPQwhubkxP2OpwQJSGlFKUaBVNxgFoFkdAlmzm6f8Mu3V9lChoBmgJaA9DCGowDcNHFW1AlIaUUpRoFU0dAmgWR0CWboLgGbCrdX2UKGgGaAloD0MITI47pYMYbECUhpRSlGgVTXsBaBZHQJZzWK2rn1Z1fZQoaAZoCWgPQwjhCFIpdtNRQJSGlFKUaBVL1mgWR0CWdnK6nR9gdX2UKGgGaAloD0MITMPwEfE/cECUhpRSlGgVTS0BaBZHQJZ3H/4qPOp1fZQoaAZoCWgPQwhZMsfyrmldQJSGlFKUaBVN6ANoFkdAlnd1WGRFJHV9lChoBmgJaA9DCDIdOj0vTHBAlIaUUpRoFU2CAWgWR0CWd5WCEpRXdX2UKGgGaAloD0MI/0C5bR/vcECUhpRSlGgVTeECaBZHQJZ32CnP3SN1fZQoaAZoCWgPQwg9npYfOA9vQJSGlFKUaBVNmgNoFkdAlntUyHmA9XV9lChoBmgJaA9DCJ5EhH/RWnBAlIaUUpRoFU1fAWgWR0CWfPdo371qdX2UKGgGaAloD0MIQlpj0AkkbECUhpRSlGgVTckCaBZHQJaDRFF2FFl1fZQoaAZoCWgPQwgNx/MZUF9GQJSGlFKUaBVL+WgWR0CWhWm6XjU/dX2UKGgGaAloD0MIXwoPmp3ecECUhpRSlGgVTSIBaBZHQJaG+VhTfix1fZQoaAZoCWgPQwgdyeU/pFFxQJSGlFKUaBVNNQJoFkdAlolaY/mknHV9lChoBmgJaA9DCKErEah+Z3FAlIaUUpRoFU3nAWgWR0CWipNPP9k0dX2UKGgGaAloD0MImYQLeYRNb0CUhpRSlGgVTcwDaBZHQJaPawnpjc51fZQoaAZoCWgPQwjKNJpcDOJwQJSGlFKUaBVN5AFoFkdAlo+vGdZq23V9lChoBmgJaA9DCPmekQiNCkJAlIaUUpRoFUvwaBZHQJaRmWa+evp1fZQoaAZoCWgPQwjJrN7hdjNwQJSGlFKUaBVNLgFoFkdAlpexsVLzw3V9lChoBmgJaA9DCOfDswQZ1mxAlIaUUpRoFU1EAmgWR0CWmdJng5zYdX2UKGgGaAloD0MIpPs5Bfkyb0CUhpRSlGgVTeYBaBZHQJaaADFId2h1fZQoaAZoCWgPQwjmXIqrSvFhQJSGlFKUaBVN6ANoFkdAlpumAbyYonV9lChoBmgJaA9DCEW8df7tIG9AlIaUUpRoFU0zAmgWR0CWnIE8aGYbdX2UKGgGaAloD0MIi8Iuip6ecECUhpRSlGgVTVwBaBZHQJae8/LTx5N1fZQoaAZoCWgPQwj1gk9z8mBfQJSGlFKUaBVN6ANoFkdAlqJI0hvBJ3V9lChoBmgJaA9DCGvylNX0amJAlIaUUpRoFU3oA2gWR0CWpOU1yeZodX2UKGgGaAloD0MIvJS6ZBxjSUCUhpRSlGgVS9hoFkdAlqX7TUiIL3V9lChoBmgJaA9DCJ2BkZe1lGBAlIaUUpRoFU3oA2gWR0CWppjqOcUedX2UKGgGaAloD0MIyHn/H6f3cECUhpRSlGgVTUQDaBZHQJanwZP2wmp1fZQoaAZoCWgPQwibkqzD0WRuQJSGlFKUaBVNvAFoFkdAlqsRInSfDnV9lChoBmgJaA9DCMPzUrEx+F1AlIaUUpRoFU3oA2gWR0CWsTS/j81odX2UKGgGaAloD0MIWWyTisZZcECUhpRSlGgVTQ4BaBZHQJa2xzvJA+p1fZQoaAZoCWgPQwhJLCl3X1VxQJSGlFKUaBVN6gFoFkdAlrpME7nxKHV9lChoBmgJaA9DCAwBwLFnFG1AlIaUUpRoFU0+AmgWR0CWvPg62fCidX2UKGgGaAloD0MI+IvZklXUb0CUhpRSlGgVTVkBaBZHQJa9Ct+1Bt11ZS4="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 160,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "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 sha256:2620d92bdc029a97efb3d573d8d8d0dafb32e2bf736a771cf1ded3d4e2e7542a
+size 84893

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d48987e91ff15107d7fe0bfc3e44f82e5771bc388a1d14e085de8701c1d22167
+size 43201

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-5.4.188+-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:c28cd8b182297487bc363924f2c02dfca91803bc7ca83639f3ba1cb0cecba24d
+size 211853

results.json

@@ -0,0 +1 @@
+{"mean_reward": 224.45699946307164, "std_reward": 14.328962367001123, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-08T17:48:39.371253"}