Upload PPO LunarLander-v2 trained agent
Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +94 -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 +3 -0
- results.json +1 -0
.gitattributes
CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.mp4 filter=lfs diff=lfs merge=lfs -text
|
README.md
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
- metrics:
|
12 |
+
- type: mean_reward
|
13 |
+
value: 213.17 +/- 16.03
|
14 |
+
name: mean_reward
|
15 |
+
task:
|
16 |
+
type: reinforcement-learning
|
17 |
+
name: reinforcement-learning
|
18 |
+
dataset:
|
19 |
+
name: LunarLander-v2
|
20 |
+
type: LunarLander-v2
|
21 |
+
---
|
22 |
+
|
23 |
+
# **PPO** Agent playing **LunarLander-v2**
|
24 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
25 |
+
|
26 |
+
## Usage (with Stable-baselines3)
|
27 |
+
TODO: Add your code
|
28 |
+
|
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 0x7f80028c4560>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f80028c45f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f80028c4680>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f80028c4710>", "_build": "<function ActorCriticPolicy._build at 0x7f80028c47a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f80028c4830>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f80028c48c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f80028c4950>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f80028c49e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f80028c4a70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f80028c4b00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f80029163c0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651807189.2874203, "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": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 124, "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-5.4.188+-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
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3abc8d996bfa975e93f4af26fe5671812f182cae86867397bc99fb3b599f8e1e
|
3 |
+
size 144044
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
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 0x7f80028c4560>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f80028c45f0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f80028c4680>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f80028c4710>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f80028c47a0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f80028c4830>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f80028c48c0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f80028c4950>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f80028c49e0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f80028c4a70>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f80028c4b00>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f80029163c0>"
|
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": 16,
|
45 |
+
"num_timesteps": 507904,
|
46 |
+
"_total_timesteps": 500000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1651807189.2874203,
|
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:": "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"
|
60 |
+
},
|
61 |
+
"_last_episode_starts": {
|
62 |
+
":type:": "<class 'numpy.ndarray'>",
|
63 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
64 |
+
},
|
65 |
+
"_last_original_obs": null,
|
66 |
+
"_episode_num": 0,
|
67 |
+
"use_sde": false,
|
68 |
+
"sde_sample_freq": -1,
|
69 |
+
"_current_progress_remaining": -0.015808000000000044,
|
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": 124,
|
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 sha256:73677e54c36d4ba469e296025ca18dcbc2cd981ba1367d6f0a7879ffc9a73c11
|
3 |
+
size 84829
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a61ad675f2373a25ce687eaa45074b0980c067cf07cf3a7b6b62fd90000ddaa5
|
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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
|
2 |
+
Python: 3.7.13
|
3 |
+
Stable-Baselines3: 1.5.0
|
4 |
+
PyTorch: 1.11.0+cu113
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:292b7baaab6c0b8fbdf1902a2242dbfe3ad8dfb0f9f2b0d1bd604aad176c1950
|
3 |
+
size 263390
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 213.17044689567305, "std_reward": 16.030162870278033, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T03:59:58.423321"}
|