Upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +96 -0
- ppo-LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2/policy.pth +3 -0
- ppo-LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -0
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: 248.09 +/- 18.98
|
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 0x7f9c0b379160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9c0b3791f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9c0b379280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9c0b379310>", "_build": "<function ActorCriticPolicy._build at 0x7f9c0b3793a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f9c0b379430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9c0b3794c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9c0b379550>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9c0b3795e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9c0b379670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9c0b379700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9c0b379790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9c0b378a80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682413573925900658, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:7340dab1f1a6ca29c9bce0beb4d2a062e9f920634c91bbfa900eed629fd2645d
|
3 |
+
size 147379
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.8.0
|
ppo-LunarLander-v2/data
ADDED
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 0x7f9c0b379160>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9c0b3791f0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9c0b379280>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9c0b379310>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f9c0b3793a0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f9c0b379430>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9c0b3794c0>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9c0b379550>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f9c0b3795e0>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9c0b379670>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9c0b379700>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9c0b379790>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f9c0b378a80>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"num_timesteps": 1015808,
|
25 |
+
"_total_timesteps": 1000000,
|
26 |
+
"_num_timesteps_at_start": 0,
|
27 |
+
"seed": null,
|
28 |
+
"action_noise": null,
|
29 |
+
"start_time": 1682413573925900658,
|
30 |
+
"learning_rate": 0.0003,
|
31 |
+
"tensorboard_log": null,
|
32 |
+
"lr_schedule": {
|
33 |
+
":type:": "<class 'function'>",
|
34 |
+
":serialized:": "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"
|
35 |
+
},
|
36 |
+
"_last_obs": {
|
37 |
+
":type:": "<class 'numpy.ndarray'>",
|
38 |
+
":serialized:": "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"
|
39 |
+
},
|
40 |
+
"_last_episode_starts": {
|
41 |
+
":type:": "<class 'numpy.ndarray'>",
|
42 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
43 |
+
},
|
44 |
+
"_last_original_obs": null,
|
45 |
+
"_episode_num": 0,
|
46 |
+
"use_sde": false,
|
47 |
+
"sde_sample_freq": -1,
|
48 |
+
"_current_progress_remaining": -0.015808000000000044,
|
49 |
+
"_stats_window_size": 100,
|
50 |
+
"ep_info_buffer": {
|
51 |
+
":type:": "<class 'collections.deque'>",
|
52 |
+
":serialized:": "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"
|
53 |
+
},
|
54 |
+
"ep_success_buffer": {
|
55 |
+
":type:": "<class 'collections.deque'>",
|
56 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
57 |
+
},
|
58 |
+
"_n_updates": 248,
|
59 |
+
"observation_space": {
|
60 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
61 |
+
":serialized:": "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",
|
62 |
+
"dtype": "float32",
|
63 |
+
"_shape": [
|
64 |
+
8
|
65 |
+
],
|
66 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
67 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
68 |
+
"bounded_below": "[False False False False False False False False]",
|
69 |
+
"bounded_above": "[False False False False False False False False]",
|
70 |
+
"_np_random": null
|
71 |
+
},
|
72 |
+
"action_space": {
|
73 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
74 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
75 |
+
"n": 4,
|
76 |
+
"_shape": [],
|
77 |
+
"dtype": "int64",
|
78 |
+
"_np_random": null
|
79 |
+
},
|
80 |
+
"n_envs": 16,
|
81 |
+
"n_steps": 1024,
|
82 |
+
"gamma": 0.999,
|
83 |
+
"gae_lambda": 0.98,
|
84 |
+
"ent_coef": 0.01,
|
85 |
+
"vf_coef": 0.5,
|
86 |
+
"max_grad_norm": 0.5,
|
87 |
+
"batch_size": 64,
|
88 |
+
"n_epochs": 4,
|
89 |
+
"clip_range": {
|
90 |
+
":type:": "<class 'function'>",
|
91 |
+
":serialized:": "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"
|
92 |
+
},
|
93 |
+
"clip_range_vf": null,
|
94 |
+
"normalize_advantage": true,
|
95 |
+
"target_kl": null
|
96 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:53dc3cf7b767a564a4b3f8f8a2ae0e31565c6a9a3b45529e00ef87d663e95ced
|
3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:53c4db04d0f8dc233dd723fdfcd8b856514ed8b34d7f4c6e6ef1db58a74cdca2
|
3 |
+
size 43329
|
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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
2 |
+
- Python: 3.9.16
|
3 |
+
- Stable-Baselines3: 1.8.0
|
4 |
+
- PyTorch: 2.0.0+cu118
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.22.4
|
7 |
+
- Gym: 0.21.0
|
replay.mp4
ADDED
Binary file (239 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 248.08617957452628, "std_reward": 18.977936058013103, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-25T09:38:25.839657"}
|