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: 256.23 +/- 14.87
|
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 0x7fa9c8517560>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa9c85175f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa9c8517680>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa9c8517710>", "_build": "<function ActorCriticPolicy._build at 0x7fa9c85177a0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa9c8517830>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa9c85178c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa9c8517950>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa9c85179e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa9c8517a70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa9c8517b00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa9c8553ea0>"}, "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": 1652183959.6804562, "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": 248, "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 sha256:626397ae1385631db304de04442f0fbf9e0f95bd236bbb1030ebd809b10faf80
|
3 |
+
size 144050
|
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 0x7fa9c8517560>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa9c85175f0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa9c8517680>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa9c8517710>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fa9c85177a0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fa9c8517830>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa9c85178c0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fa9c8517950>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa9c85179e0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa9c8517a70>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa9c8517b00>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fa9c8553ea0>"
|
20 |
+
},
|
21 |
+
"verbose": 1,
|
22 |
+
"policy_kwargs": {},
|
23 |
+
"observation_space": {
|
24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
25 |
+
":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
|
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": 1015808,
|
46 |
+
"_total_timesteps": 1000000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1652183959.6804562,
|
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": 248,
|
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 sha256:1ea53ae6b5391780e6f9a4d934262905bb018aae77184563f5699e9e08887327
|
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:c6839c41aaa96246a51b16c6554dcc3e6f7a00c8c9454a4214501e933b96a43f
|
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 sha256:215ca3e4f7f1dcce8b4984f71a48dc22064362a1f0e8cff7e4362b9c806e283d
|
3 |
+
size 235595
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 256.22752122994365, "std_reward": 14.872470035996752, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-10T12:22:02.792514"}
|