jeinsong commited on
Commit
4f70514
1 Parent(s): 988259e

Upload PPO LunarLander-v2 trained agent

Browse files
README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - LunarLander-v2
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: PPO
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
13
+ name: reinforcement-learning
14
+ dataset:
15
+ name: LunarLander-v2
16
+ type: LunarLander-v2
17
+ metrics:
18
+ - type: mean_reward
19
+ value: 273.84 +/- 12.44
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **PPO** Agent playing **LunarLander-v2**
25
+ This is a trained model of a **PPO** agent playing **LunarLander-v2**
26
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
27
+
28
+ ## Usage (with Stable-baselines3)
29
+ TODO: Add your code
30
+
31
+
32
+ ```python
33
+ from stable_baselines3 import ...
34
+ from huggingface_sb3 import load_from_hub
35
+
36
+ ...
37
+ ```
config.json ADDED
@@ -0,0 +1 @@
 
 
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 0x7f508fe06550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f508fe065e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f508fe06670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f508fe06700>", "_build": "<function ActorCriticPolicy._build at 0x7f508fe06790>", "forward": "<function ActorCriticPolicy.forward at 0x7f508fe06820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f508fe068b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f508fe06940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f508fe069d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f508fe06a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f508fe06af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f508fe06b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f508fdfdcf0>"}, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1673966686007589691, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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:": "gAWVJRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBOj3/Vt0ckCUhpRSlIwBbJRL4YwBdJRHQJ4lHqTr3TN1fZQoaAZoCWgPQwgLluoC3lZyQJSGlFKUaBVL2mgWR0CeJSnTiKixdX2UKGgGaAloD0MIpkQSvQxqZUCUhpRSlGgVTegDaBZHQJ4lbtRekYZ1fZQoaAZoCWgPQwjWAntMZE9xQJSGlFKUaBVL22gWR0CeJhyRSxZ/dX2UKGgGaAloD0MI3Qn2X+cQcUCUhpRSlGgVS+NoFkdAniYq3mV7hXV9lChoBmgJaA9DCNxmKsTjx3JAlIaUUpRoFUvDaBZHQJ4mTljmSyN1fZQoaAZoCWgPQwgR/G8le4BzQJSGlFKUaBVL12gWR0CeJnfgaWHDdX2UKGgGaAloD0MIdsB1xUyEcECUhpRSlGgVS8xoFkdAnieAjQiRn3V9lChoBmgJaA9DCFoSoKbWUHFAlIaUUpRoFUu7aBZHQJ4nzR4QjD91fZQoaAZoCWgPQwhpVUs6SnNvQJSGlFKUaBVLyGgWR0CeKAkLhJiBdX2UKGgGaAloD0MIFobI6Wvab0CUhpRSlGgVS8RoFkdAnigoF/x2CHV9lChoBmgJaA9DCPLvMy4cbnJAlIaUUpRoFUvxaBZHQJ4pFOZb6gx1fZQoaAZoCWgPQwjohxHC42NzQJSGlFKUaBVNBwFoFkdAnikvYWcjJXV9lChoBmgJaA9DCDVCP1Mvg3JAlIaUUpRoFUvOaBZHQJ4pmsmv4dp1fZQoaAZoCWgPQwj12myshLZwQJSGlFKUaBVLuWgWR0CeKbajesPrdX2UKGgGaAloD0MIsyYW+Io9cUCUhpRSlGgVS8ZoFkdAnin8TnJT2nV9lChoBmgJaA9DCKIpO/3g8HFAlIaUUpRoFUvSaBZHQJ4qN5qubI91fZQoaAZoCWgPQwib5bLRuYFvQJSGlFKUaBVLymgWR0CeKlsRQJokdX2UKGgGaAloD0MI+DjThO1SckCUhpRSlGgVS7NoFkdAnirS9h7VrnV9lChoBmgJaA9DCKm8HeE0NnBAlIaUUpRoFUvLaBZHQJ4rN/2Cdz51fZQoaAZoCWgPQwgCEHf1KvZyQJSGlFKUaBVL1mgWR0CeK1GiHqNZdX2UKGgGaAloD0MIJsgIqDDSc0CUhpRSlGgVTQ0BaBZHQJ5EYZBLPD51fZQoaAZoCWgPQwitbB/yFrtwQJSGlFKUaBVL4WgWR0CeRRl4C6pYdX2UKGgGaAloD0MIWUxsPu77cECUhpRSlGgVS9RoFkdAnkUZxrBTGnV9lChoBmgJaA9DCKuxhLUx9VFAlIaUUpRoFUuOaBZHQJ5FQSM98qp1fZQoaAZoCWgPQwjvA5DahKlwQJSGlFKUaBVL9GgWR0CeRUefI0ZWdX2UKGgGaAloD0MILbMIxdaDc0CUhpRSlGgVS+doFkdAnkV0i+tbLXV9lChoBmgJaA9DCDxqTIh5jnJAlIaUUpRoFUu/aBZHQJ5Fh/x2B8R1fZQoaAZoCWgPQwhrKSDtf6xNQJSGlFKUaBVLnGgWR0CeRfy/KyOadX2UKGgGaAloD0MIR+hn6vUnb0CUhpRSlGgVS81oFkdAnkYDewcHW3V9lChoBmgJaA9DCB9JSQ9DdG5AlIaUUpRoFUvWaBZHQJ5GoLronrp1fZQoaAZoCWgPQwgaTS7GgN1yQJSGlFKUaBVL2mgWR0CeRtGHHmzTdX2UKGgGaAloD0MIlnmrroM6cUCUhpRSlGgVS9BoFkdAnkey7f51vHV9lChoBmgJaA9DCI/HDFTG90lAlIaUUpRoFU3oA2gWR0CeSCMM7U5NdX2UKGgGaAloD0MINX12wLULc0CUhpRSlGgVS/poFkdAnkg477sOXnV9lChoBmgJaA9DCEAwR49fAHJAlIaUUpRoFUvaaBZHQJ5IbIOpbUx1fZQoaAZoCWgPQwj3rdaJS5VuQJSGlFKUaBVLsmgWR0CeSNYtQKrrdX2UKGgGaAloD0MI6pYd4p+GcUCUhpRSlGgVTQQBaBZHQJ5JZljEvTR1fZQoaAZoCWgPQwgbu0T1luZxQJSGlFKUaBVLx2gWR0CeSe9bHIZJdX2UKGgGaAloD0MI3Zp0W+JqcUCUhpRSlGgVS8doFkdAnkoP0mMOw3V9lChoBmgJaA9DCFCMLJkjTXFAlIaUUpRoFUvMaBZHQJ5KC+wkgOl1fZQoaAZoCWgPQwj7IMuCSYJwQJSGlFKUaBVLxmgWR0CeSjTEit7sdX2UKGgGaAloD0MITWiSWNLOcECUhpRSlGgVS8hoFkdAnkpRgiNbT3V9lChoBmgJaA9DCA360tufGHBAlIaUUpRoFUviaBZHQJ5Klnyup0h1fZQoaAZoCWgPQwi8ICI1LRJxQJSGlFKUaBVLwWgWR0CeSwGtITXbdX2UKGgGaAloD0MInx7bMiAHcUCUhpRSlGgVS95oFkdAnksLsOXmeXV9lChoBmgJaA9DCFUxlX7CS3NAlIaUUpRoFUvzaBZHQJ5LcOrhisp1fZQoaAZoCWgPQwgP7zmwnGxvQJSGlFKUaBVL2mgWR0CeS6sH0K7adX2UKGgGaAloD0MIE4HqH8R2cECUhpRSlGgVS+hoFkdAnky/rnkkr3V9lChoBmgJaA9DCPrQBfVtWXBAlIaUUpRoFUvZaBZHQJ5MzeoDPnl1fZQoaAZoCWgPQwh5I/PIn/BxQJSGlFKUaBVL32gWR0CeTTLkS26TdX2UKGgGaAloD0MIGVdcHJWAc0CUhpRSlGgVS89oFkdAnk1PWpZOi3V9lChoBmgJaA9DCJMehlbnPnBAlIaUUpRoFUvzaBZHQJ5NX8sMAm11fZQoaAZoCWgPQwjdmJ6wBPFyQJSGlFKUaBVLxGgWR0CeTiw1R+BpdX2UKGgGaAloD0MIARb59cMJcECUhpRSlGgVS8loFkdAnk6aH446wXV9lChoBmgJaA9DCGaFIt1PzHFAlIaUUpRoFUvxaBZHQJ5OmqXF98Z1fZQoaAZoCWgPQwjGM2joX0pyQJSGlFKUaBVLwWgWR0CeTsnO0LMLdX2UKGgGaAloD0MI0CUceosjc0CUhpRSlGgVS+toFkdAnk77vPTodXV9lChoBmgJaA9DCFIKur3kBXRAlIaUUpRoFUvoaBZHQJ5PCq814xF1fZQoaAZoCWgPQwgxYTQrW6FyQJSGlFKUaBVLuWgWR0CeTxqoIfKZdX2UKGgGaAloD0MIWCB6UqZFbkCUhpRSlGgVS7xoFkdAnk8vrWy1NXV9lChoBmgJaA9DCFx1HappPnFAlIaUUpRoFUvvaBZHQJ5PSH6/IsB1fZQoaAZoCWgPQwibj2tDhbVwQJSGlFKUaBVL0GgWR0CeUBvQnhKldX2UKGgGaAloD0MIATCeQYPbcUCUhpRSlGgVS+xoFkdAnlB3O0LMLXV9lChoBmgJaA9DCFG8ytrmLHJAlIaUUpRoFUu7aBZHQJ5Qu4vvjOt1fZQoaAZoCWgPQwiZRSi2QqVwQJSGlFKUaBVLw2gWR0CeUNlfJFLGdX2UKGgGaAloD0MI4c/wZs3CckCUhpRSlGgVS8NoFkdAnlEqneizs3V9lChoBmgJaA9DCGwhyEGJjXFAlIaUUpRoFUvDaBZHQJ5RQgPmPo51fZQoaAZoCWgPQwjarWUynGdyQJSGlFKUaBVL2mgWR0CeUb/FzdULdX2UKGgGaAloD0MIaLJ/ngbzcECUhpRSlGgVS7poFkdAnlI6OT7l73V9lChoBmgJaA9DCFVQUfUr9G5AlIaUUpRoFUvFaBZHQJ5S8NWluWN1fZQoaAZoCWgPQwhjtmRVRN1wQJSGlFKUaBVL2GgWR0CeUwqGUOd5dX2UKGgGaAloD0MI4ltYN54ocECUhpRSlGgVS75oFkdAnlMJ1eSjg3V9lChoBmgJaA9DCDRN2H4y3HJAlIaUUpRoFU0CAWgWR0CeU2F7D2rXdX2UKGgGaAloD0MIGHjuPdxIc0CUhpRSlGgVS/RoFkdAnlN5vUBnz3V9lChoBmgJaA9DCD7shQI2oHJAlIaUUpRoFUvjaBZHQJ5Tf1bqyGB1fZQoaAZoCWgPQwiqCg3E8ltzQJSGlFKUaBVL3mgWR0CeU43zcynDdX2UKGgGaAloD0MIuk4jLRXvb0CUhpRSlGgVS8NoFkdAnlQB0U47zXV9lChoBmgJaA9DCLR1cLD3mXJAlIaUUpRoFU0IAWgWR0CeVChCtzS1dX2UKGgGaAloD0MImtL6W4I8cUCUhpRSlGgVS71oFkdAnlR+89Oh03V9lChoBmgJaA9DCKIKf4Y3K3FAlIaUUpRoFUvDaBZHQJ5Uu2OQyRB1fZQoaAZoCWgPQwg17PfE+pdzQJSGlFKUaBVL2WgWR0CeVMbDdgv2dX2UKGgGaAloD0MIehubHaltUECUhpRSlGgVS7doFkdAnlTqyjYZmHV9lChoBmgJaA9DCI6s/DJYdHFAlIaUUpRoFUvVaBZHQJ5VXrTpgTh1fZQoaAZoCWgPQwgXLUDbaghxQJSGlFKUaBVLwWgWR0CeVgf4REncdX2UKGgGaAloD0MIc6Hyr2X8cECUhpRSlGgVS9toFkdAnlYU+s5n13V9lChoBmgJaA9DCFBSYAFM325AlIaUUpRoFUu9aBZHQJ5Wr74zrNZ1fZQoaAZoCWgPQwhMx5xn7IhvQJSGlFKUaBVLx2gWR0CeV2KYiPhidX2UKGgGaAloD0MISmHe48yUckCUhpRSlGgVS+RoFkdAnldueWfK6nV9lChoBmgJaA9DCNEDH4NV/XJAlIaUUpRoFUvRaBZHQJ5XePDHfdh1fZQoaAZoCWgPQwi05sdfWk5yQJSGlFKUaBVL0GgWR0CeV4xzJZGKdX2UKGgGaAloD0MIaAdcV8xCckCUhpRSlGgVS8xoFkdAnlePYjB2wHV9lChoBmgJaA9DCDBJZYq5MnJAlIaUUpRoFUu5aBZHQJ5YOnDR+jN1fZQoaAZoCWgPQwj0wwjhEe1yQJSGlFKUaBVNBAFoFkdAnlg/ustCiXV9lChoBmgJaA9DCC8X8Z0YwW5AlIaUUpRoFUvHaBZHQJ5Yz2dupCN1fZQoaAZoCWgPQwgjg9xF2CZwQJSGlFKUaBVLxmgWR0CeWNyQPqcFdX2UKGgGaAloD0MIICi37XvNc0CUhpRSlGgVS/RoFkdAnlkFf3N9pnV9lChoBmgJaA9DCOFh2je3Y3BAlIaUUpRoFUvUaBZHQJ5Z3vmYBvJ1fZQoaAZoCWgPQwiFmbZ/5TxxQJSGlFKUaBVL/GgWR0CeWjg62fCidX2UKGgGaAloD0MIPX/aqI42cUCUhpRSlGgVS79oFkdAnloxx5s0pHVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 620, "n_steps": 1024, "gamma": 0.99, "gae_lambda": 0.99, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 5, "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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
ppo-LunarLander-v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c88eeb10a76d14dfb6fd6ac390fcd7ee38e058779eaf472c8c0241ee3e1e90db
3
+ size 147303
ppo-LunarLander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
ppo-LunarLander-v2/data ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "policy_class": {
3
+ ":type:": "<class 'abc.ABCMeta'>",
4
+ ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
5
+ "__module__": "stable_baselines3.common.policies",
6
+ "__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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7f508fe06550>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f508fe065e0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f508fe06670>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f508fe06700>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f508fe06790>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f508fe06820>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f508fe068b0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f508fe06940>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f508fe069d0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f508fe06a60>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f508fe06af0>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f508fe06b80>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7f508fdfdcf0>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "observation_space": {
25
+ ":type:": "<class 'gym.spaces.box.Box'>",
26
+ ":serialized:": "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",
27
+ "dtype": "float32",
28
+ "_shape": [
29
+ 8
30
+ ],
31
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
32
+ "high": "[inf inf inf inf inf inf inf inf]",
33
+ "bounded_below": "[False False False False False False False False]",
34
+ "bounded_above": "[False False False False False False False False]",
35
+ "_np_random": null
36
+ },
37
+ "action_space": {
38
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
39
+ ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
40
+ "n": 4,
41
+ "_shape": [],
42
+ "dtype": "int64",
43
+ "_np_random": null
44
+ },
45
+ "n_envs": 16,
46
+ "num_timesteps": 1015808,
47
+ "_total_timesteps": 1000000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1673966686007589691,
52
+ "learning_rate": 0.0003,
53
+ "tensorboard_log": null,
54
+ "lr_schedule": {
55
+ ":type:": "<class 'function'>",
56
+ ":serialized:": "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"
57
+ },
58
+ "_last_obs": {
59
+ ":type:": "<class 'numpy.ndarray'>",
60
+ ":serialized:": "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"
61
+ },
62
+ "_last_episode_starts": {
63
+ ":type:": "<class 'numpy.ndarray'>",
64
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
65
+ },
66
+ "_last_original_obs": null,
67
+ "_episode_num": 0,
68
+ "use_sde": false,
69
+ "sde_sample_freq": -1,
70
+ "_current_progress_remaining": -0.015808000000000044,
71
+ "ep_info_buffer": {
72
+ ":type:": "<class 'collections.deque'>",
73
+ ":serialized:": "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"
74
+ },
75
+ "ep_success_buffer": {
76
+ ":type:": "<class 'collections.deque'>",
77
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
78
+ },
79
+ "_n_updates": 620,
80
+ "n_steps": 1024,
81
+ "gamma": 0.99,
82
+ "gae_lambda": 0.99,
83
+ "ent_coef": 0.01,
84
+ "vf_coef": 0.5,
85
+ "max_grad_norm": 0.5,
86
+ "batch_size": 64,
87
+ "n_epochs": 5,
88
+ "clip_range": {
89
+ ":type:": "<class 'function'>",
90
+ ":serialized:": "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"
91
+ },
92
+ "clip_range_vf": null,
93
+ "normalize_advantage": true,
94
+ "target_kl": null
95
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:525ac3d02034b21ee387d5b6a077c789b64c5cf828e1a272c709127a760ca50a
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5addcb52bcce7a305e52b1b03c683f4b1d7e0c66ee0fd61906d94bb4780bb218
3
+ size 43393
ppo-LunarLander-v2/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
ppo-LunarLander-v2/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.8.16
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.0+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.21.6
7
+ - Gym: 0.21.0
replay.mp4 ADDED
Binary file (235 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 273.84139681578955, "std_reward": 12.436656625479237, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-17T14:58:24.125918"}