irenekar commited on
Commit
6c1eb2c
1 Parent(s): 8fd32f6

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: 216.24 +/- 80.71
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 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 0x7fcc56cd6ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcc56cd6d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcc56cd6dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcc56cd6e50>", "_build": "<function ActorCriticPolicy._build at 0x7fcc56cd6ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7fcc56cd6f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcc56cdb040>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcc56cdb0d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcc56cdb160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcc56cdb1f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcc56cdb280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcc56d4ff00>"}, "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": 500736, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670226128606823553, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAPVlmb7/QAM/muCqPQlnWr6KffW7j27LPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.0014719999999999178, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1956, "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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "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:aa5ba65f12774bc8354d8e91c7b1949d7c94ff53e0a5b433e14e9ca6cb39e6ea
3
+ size 146493
ppo-LunarLander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.2
ppo-LunarLander-v2/data ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7fcc56cd6ca0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcc56cd6d30>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcc56cd6dc0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcc56cd6e50>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fcc56cd6ee0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fcc56cd6f70>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcc56cdb040>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fcc56cdb0d0>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcc56cdb160>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcc56cdb1f0>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcc56cdb280>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7fcc56d4ff00>"
20
+ },
21
+ "verbose": 1,
22
+ "policy_kwargs": {},
23
+ "observation_space": {
24
+ ":type:": "<class 'gym.spaces.box.Box'>",
25
+ ":serialized:": "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",
26
+ "dtype": "float32",
27
+ "_shape": [
28
+ 8
29
+ ],
30
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
31
+ "high": "[inf inf inf inf inf inf inf inf]",
32
+ "bounded_below": "[False False False False False False False False]",
33
+ "bounded_above": "[False False False False False False False False]",
34
+ "_np_random": null
35
+ },
36
+ "action_space": {
37
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
38
+ ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
39
+ "n": 4,
40
+ "_shape": [],
41
+ "dtype": "int64",
42
+ "_np_random": null
43
+ },
44
+ "n_envs": 1,
45
+ "num_timesteps": 500736,
46
+ "_total_timesteps": 500000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1670226128606823553,
51
+ "learning_rate": 0.0003,
52
+ "tensorboard_log": null,
53
+ "lr_schedule": {
54
+ ":type:": "<class 'function'>",
55
+ ":serialized:": "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"
56
+ },
57
+ "_last_obs": {
58
+ ":type:": "<class 'numpy.ndarray'>",
59
+ ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAPVlmb7/QAM/muCqPQlnWr6KffW7j27LPAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
60
+ },
61
+ "_last_episode_starts": {
62
+ ":type:": "<class 'numpy.ndarray'>",
63
+ ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
64
+ },
65
+ "_last_original_obs": null,
66
+ "_episode_num": 0,
67
+ "use_sde": false,
68
+ "sde_sample_freq": -1,
69
+ "_current_progress_remaining": -0.0014719999999999178,
70
+ "ep_info_buffer": {
71
+ ":type:": "<class 'collections.deque'>",
72
+ ":serialized:": "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"
73
+ },
74
+ "ep_success_buffer": {
75
+ ":type:": "<class 'collections.deque'>",
76
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
77
+ },
78
+ "_n_updates": 1956,
79
+ "n_steps": 1024,
80
+ "gamma": 0.999,
81
+ "gae_lambda": 0.98,
82
+ "ent_coef": 0.01,
83
+ "vf_coef": 0.5,
84
+ "max_grad_norm": 0.5,
85
+ "batch_size": 64,
86
+ "n_epochs": 4,
87
+ "clip_range": {
88
+ ":type:": "<class 'function'>",
89
+ ":serialized:": "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"
90
+ },
91
+ "clip_range_vf": null,
92
+ "normalize_advantage": true,
93
+ "target_kl": null
94
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7b6874c6adad09d82d0a8a88967a18aeeafac2b0c1204119d423822106d7661f
3
+ size 87865
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:13fa6fcc9df93dd66665f367a319ad0a0cc7cc0e60e4bc2e68c57e94586781d7
3
+ size 43201
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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
2
+ Python: 3.8.15
3
+ Stable-Baselines3: 1.6.2
4
+ PyTorch: 1.12.1+cu113
5
+ GPU Enabled: True
6
+ Numpy: 1.21.6
7
+ Gym: 0.21.0
replay.mp4 ADDED
Binary file (226 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 216.2378487274087, "std_reward": 80.71267432764539, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-05T08:06:17.388964"}