Initial commit

.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: 217.04 +/- 33.19
+      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 0x7f1e65917170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1e65917200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1e65917290>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1e65917320>", "_build": "<function ActorCriticPolicy._build at 0x7f1e659173b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f1e65917440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1e659174d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1e65917560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1e659175f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1e65917680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1e65917710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1e65960780>"}, "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": 1652757959.9649286, "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-mlpp.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc73b54573b1eb609cbc3d63a14c00b14e674609a4b22478f0bcc5b212887eaa
+size 144106

ppo-mlpp/_stable_baselines3_version

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

ppo-mlpp/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 0x7f1e65917170>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1e65917200>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1e65917290>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1e65917320>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f1e659173b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f1e65917440>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1e659174d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f1e65917560>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1e659175f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1e65917680>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1e65917710>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f1e65960780>"
+    },
+    "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": 1652757959.9649286,
+    "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:": "gAWVfhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIrDb/rzo4W0CUhpRSlIwBbJRN6AOMAXSUR0CQzgOyVv/BdX2UKGgGaAloD0MI98jmqnnrXUCUhpRSlGgVTegDaBZHQJDQgE4ecQR1fZQoaAZoCWgPQwjIRbWIKBtjQJSGlFKUaBVN6ANoFkdAkNaW5c1O03V9lChoBmgJaA9DCOYklL4QplpAlIaUUpRoFU3oA2gWR0CQ3ZYZl4C7dX2UKGgGaAloD0MIf2jmyTXwUECUhpRSlGgVTegDaBZHQJDiw8PnSv11fZQoaAZoCWgPQwh5OleUkihiQJSGlFKUaBVN6ANoFkdAkOfUXYUWVXV9lChoBmgJaA9DCBdi9UcYvV1AlIaUUpRoFU3oA2gWR0CQ6Dkc0cfedX2UKGgGaAloD0MIamgDsAH8XkCUhpRSlGgVTegDaBZHQJDpjuv2XcB1fZQoaAZoCWgPQwhcyvli78FaQJSGlFKUaBVN6ANoFkdAkOy9iDujRHV9lChoBmgJaA9DCKOP+YBAxl9AlIaUUpRoFU3oA2gWR0CQ72ovSMLndX2UKGgGaAloD0MIpwUv+gqxXUCUhpRSlGgVTegDaBZHQJD3OpqASWZ1fZQoaAZoCWgPQwi6EoHqH9FcQJSGlFKUaBVN6ANoFkdAkP/SYLLIP3V9lChoBmgJaA9DCCdsPxnjRV9AlIaUUpRoFU3oA2gWR0CRAQvy9VWCdX2UKGgGaAloD0MIrz4e+u66NECUhpRSlGgVTcwDaBZHQJEB6t6ol2N1fZQoaAZoCWgPQwiDM/j7xbhhQJSGlFKUaBVN6ANoFkdAkQx3rY5DJHV9lChoBmgJaA9DCL3+JD734mBAlIaUUpRoFU3oA2gWR0CRDVcL0BfbdX2UKGgGaAloD0MIRl9BmrF4XECUhpRSlGgVTegDaBZHQJERfSThYNl1fZQoaAZoCWgPQwiSPq2iP7VaQJSGlFKUaBVN6ANoFkdAkRQ37cfvF3V9lChoBmgJaA9DCOV/8ndvxWlAlIaUUpRoFU1oAmgWR0CRFoZ6lchUdX2UKGgGaAloD0MIuFhRg2khWUCUhpRSlGgVTegDaBZHQJEalmEoOQR1fZQoaAZoCWgPQwiRfvs6cLhcQJSGlFKUaBVN6ANoFkdAkSIw/xDst3V9lChoBmgJaA9DCMDQI0bPW1ZAlIaUUpRoFU3oA2gWR0CRKABo24usdX2UKGgGaAloD0MI4JwRpb0LWkCUhpRSlGgVTegDaBZHQJEtbu0CzTp1fZQoaAZoCWgPQwjYLJeNzqNaQJSGlFKUaBVN6ANoFkdAkS3V8ohIOHV9lChoBmgJaA9DCDW0AdgA/WBAlIaUUpRoFU3oA2gWR0CRL10xdpqRdX2UKGgGaAloD0MI3ElE+BelW0CUhpRSlGgVTegDaBZHQJE2WziS7oV1fZQoaAZoCWgPQwgo9PqT+DNmQJSGlFKUaBVN6ANoFkdAkUBibQTmGXV9lChoBmgJaA9DCLByaJHtHDBAlIaUUpRoFU1KAWgWR0CRqDRYRujzdX2UKGgGaAloD0MIg/xs5Dp/YECUhpRSlGgVTegDaBZHQJGsCycCo0h1fZQoaAZoCWgPQwiFCaNZWQZiQJSGlFKUaBVN6ANoFkdAka1VOj7AL3V9lChoBmgJaA9DCLgCCvX0e1lAlIaUUpRoFU3oA2gWR0CRrk150KZ2dX2UKGgGaAloD0MI81SH3AzKVkCUhpRSlGgVTegDaBZHQJG5FAdGRV91fZQoaAZoCWgPQwhKfy+FB4pcQJSGlFKUaBVN6ANoFkdAkbn0mlZX+3V9lChoBmgJaA9DCLitLTwvEFhAlIaUUpRoFU3oA2gWR0CRvjEYfnwHdX2UKGgGaAloD0MImEwVjEpAX0CUhpRSlGgVTegDaBZHQJHA+dQO4G51fZQoaAZoCWgPQwiaPjvgupBeQJSGlFKUaBVN6ANoFkdAkcM6HsTnJXV9lChoBmgJaA9DCAa5izBFvWFAlIaUUpRoFU3oA2gWR0CRx1ZkkKNRdX2UKGgGaAloD0MIX1yq0hZLVkCUhpRSlGgVTegDaBZHQJHOuY0EX+F1fZQoaAZoCWgPQwi0HOihtn9eQJSGlFKUaBVN6ANoFkdAkdQnFglWwXV9lChoBmgJaA9DCHOiXYWUNVlAlIaUUpRoFU3oA2gWR0CR2aLU1AJLdX2UKGgGaAloD0MI0xOWeEA4VkCUhpRSlGgVTegDaBZHQJHbLVZs9B91fZQoaAZoCWgPQwgLJv4o6hdkQJSGlFKUaBVN6ANoFkdAkeIBS5y2hXV9lChoBmgJaA9DCEg17PdEcmFAlIaUUpRoFU3oA2gWR0CR62Mt9QXRdX2UKGgGaAloD0MI68VQTrS3W0CUhpRSlGgVTegDaBZHQJHxNZ1V5rx1fZQoaAZoCWgPQwgiOC7jpi1eQJSGlFKUaBVN6ANoFkdAkfRU7jkuH3V9lChoBmgJaA9DCEjDKXPzumNAlIaUUpRoFU3oA2gWR0CR9XYc/+sHdX2UKGgGaAloD0MIQUgWMAF0YUCUhpRSlGgVTegDaBZHQJH2PZZjhDR1fZQoaAZoCWgPQwgK+DWSBIxeQJSGlFKUaBVN6ANoFkdAkf8mqLjxTnV9lChoBmgJaA9DCIV6+gj83GBAlIaUUpRoFU3oA2gWR0CR/+WUr08OdX2UKGgGaAloD0MIgBE0ZhKzYkCUhpRSlGgVTegDaBZHQJIDoo/iYLN1fZQoaAZoCWgPQwgqxY7GodpWQJSGlFKUaBVN6ANoFkdAkgYQYHgP3HV9lChoBmgJaA9DCJDZWfRO7F5AlIaUUpRoFU3oA2gWR0CSCDAmzBykdX2UKGgGaAloD0MIRrbz/VR8YkCUhpRSlGgVTegDaBZHQJIMGpcX3xp1fZQoaAZoCWgPQwgF4J9SpZhgQJSGlFKUaBVN6ANoFkdAkhNcHSnccnV9lChoBmgJaA9DCO0L6IW7iGBAlIaUUpRoFU3oA2gWR0CSGIkRSP2gdX2UKGgGaAloD0MI9GxWfa67WkCUhpRSlGgVTegDaBZHQJIdqhbnoxJ1fZQoaAZoCWgPQwjuPVxyXMxiQJSGlFKUaBVN6ANoFkdAkh8ViKBNEnV9lChoBmgJaA9DCCL6tfXTgmJAlIaUUpRoFU3oA2gWR0CSJa5yEL6UdX2UKGgGaAloD0MIxYzw9qD0YECUhpRSlGgVTegDaBZHQJIuo+wC8vp1fZQoaAZoCWgPQwjbM0sC1IRjQJSGlFKUaBVN6ANoFkdAkjQt92HLzXV9lChoBmgJaA9DCIF38ukxSGVAlIaUUpRoFU3oA2gWR0CSmT2n889wdX2UKGgGaAloD0MIIVnABG7lXkCUhpRSlGgVTegDaBZHQJKaaJQ+EAZ1fZQoaAZoCWgPQwgp6zcTU2lgQJSGlFKUaBVN6ANoFkdAkpszqOcUd3V9lChoBmgJaA9DCFSqRNlbaGtAlIaUUpRoFU0EAmgWR0CSodmiQDFIdX2UKGgGaAloD0MIti41Qr/PY0CUhpRSlGgVTegDaBZHQJKjp1cMVlB1fZQoaAZoCWgPQwhBECBDx9ZXQJSGlFKUaBVN6ANoFkdAkqRT2vjfenV9lChoBmgJaA9DCEbrqGqC+WBAlIaUUpRoFU3oA2gWR0CSp2xNZeRgdX2UKGgGaAloD0MIgq0SLA4rYkCUhpRSlGgVTegDaBZHQJKpdWBBiTd1fZQoaAZoCWgPQwiOzvkpjqheQJSGlFKUaBVN6ANoFkdAkqsgWSEDhnV9lChoBmgJaA9DCGdF1EQfimNAlIaUUpRoFU3oA2gWR0CSrhX6qKgqdX2UKGgGaAloD0MI14S0xqBTLcCUhpRSlGgVS9poFkdAkq7kqlP8AXV9lChoBmgJaA9DCKsi3GTUeGJAlIaUUpRoFU3oA2gWR0CSs2wMpgCwdX2UKGgGaAloD0MIKSFYVS8sYECUhpRSlGgVTegDaBZHQJK3kGGEf1Z1fZQoaAZoCWgPQwiWlLvP8ZEMwJSGlFKUaBVLymgWR0CSuPlg+hXbdX2UKGgGaAloD0MI5ZmXw+70ZECUhpRSlGgVTegDaBZHQJK9EZaV2Rt1fZQoaAZoCWgPQwi9qN2vArQoQJSGlFKUaBVNQQFoFkdAksDJCOWBz3V9lChoBmgJaA9DCMh9q3XinltAlIaUUpRoFU3oA2gWR0CSwpqioKlYdX2UKGgGaAloD0MIhsq/llfFXUCUhpRSlGgVTegDaBZHQJLKSLQ5WBB1fZQoaAZoCWgPQwgC9Pv+TTJlQJSGlFKUaBVN6ANoFkdAks9YVqN6xHV9lChoBmgJaA9DCOFiRQ0myWBAlIaUUpRoFU3oA2gWR0CS0iz/IbOvdX2UKGgGaAloD0MIKQZINIHSYECUhpRSlGgVTegDaBZHQJLTRYHPeHl1fZQoaAZoCWgPQwi8sgsGVzRkQJSGlFKUaBVN6ANoFkdAktQSobXHznV9lChoBmgJaA9DCAu2EU92kWBAlIaUUpRoFU3oA2gWR0CS22Z4wAU+dX2UKGgGaAloD0MImiLA6V0JVkCUhpRSlGgVTegDaBZHQJLeE/Rmbsp1fZQoaAZoCWgPQwixNPCjGiRhQJSGlFKUaBVN6ANoFkdAkuHGQ0XP7nV9lChoBmgJaA9DCNMwfERMsWdAlIaUUpRoFU3oA2gWR0CS5AubI91VdX2UKGgGaAloD0MI/rW8cr2JPUCUhpRSlGgVTREBaBZHQJLkO4oZydZ1fZQoaAZoCWgPQwhdM/lmmz1EQJSGlFKUaBVNDQFoFkdAkuTK1kUbk3V9lChoBmgJaA9DCKhSswdatV9AlIaUUpRoFU3oA2gWR0CS5fALApKBdX2UKGgGaAloD0MIW0HTEiuCY0CUhpRSlGgVTegDaBZHQJLvZgx8D0V1fZQoaAZoCWgPQwh5I/PIH3BEQJSGlFKUaBVL/2gWR0CS8yNG3F1kdX2UKGgGaAloD0MI+FCiJQ9mYECUhpRSlGgVTegDaBZHQJLz30PH1e11fZQoaAZoCWgPQwhdiNUf4QlkQJSGlFKUaBVN6ANoFkdAkvVfio86m3V9lChoBmgJaA9DCBAjhEebSGFAlIaUUpRoFU3oA2gWR0CS+ammce8xdX2UKGgGaAloD0MIrRVtjnMuZECUhpRSlGgVTegDaBZHQJL9xovi97F1fZQoaAZoCWgPQwgRGsHG9UNcQJSGlFKUaBVN6ANoFkdAkv/ehwl0HXV9lChoBmgJaA9DCLmKxW8KhVtAlIaUUpRoFU3oA2gWR0CTCNRZ2ZAqdX2UKGgGaAloD0MImdamsb3cQECUhpRSlGgVTRkBaBZHQJMM7F+/gzh1fZQoaAZoCWgPQwiefeVB+sNgQJSGlFKUaBVN6ANoFkdAkw6lTm4iHXV9lChoBmgJaA9DCGnGounsh1tAlIaUUpRoFU3oA2gWR0CTEbR9PUKBdWUu"
+    },
+    "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-mlpp/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6e9b59c50fa5778e7ec957fe9b42148c3f00a9a4642b0c599b8f35719c0ffd8
+size 84893

ppo-mlpp/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b7f1fcf923592d408b62ad03d4c6ae31d95ed344c70a5f88587a169742dca478
+size 43201

ppo-mlpp/pytorch_variables.pth

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

ppo-mlpp/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 sha256:0ef6778fbbe279ceed9e83418990e767dd4577eac1836c20adc3f8d9ba68f2e9
+size 259304

results.json

@@ -0,0 +1 @@
+{"mean_reward": 217.03861842749401, "std_reward": 33.19041154996084, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-17T04:02:54.669166"}