First example
Browse files- .gitattributes +1 -0
- README.md +28 -0
- config.json +1 -0
- ppo-LunarLander-test.zip +3 -0
- ppo-LunarLander-test/_stable_baselines3_version +1 -0
- ppo-LunarLander-test/data +94 -0
- ppo-LunarLander-test/policy.optimizer.pth +3 -0
- ppo-LunarLander-test/policy.pth +3 -0
- ppo-LunarLander-test/pytorch_variables.pth +3 -0
- ppo-LunarLander-test/system_info.txt +7 -0
- replay.mp4 +3 -0
- results.json +1 -0
- test.zip +3 -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: 220.29 +/- 20.92
|
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 0x7f6451fc59e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6451fc5a70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6451fc5b00>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6451fc5b90>", "_build": "<function ActorCriticPolicy._build at 0x7f6451fc5c20>", "forward": "<function ActorCriticPolicy.forward at 0x7f6451fc5cb0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6451fc5d40>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6451fc5dd0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6451fc5e60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6451fc5ef0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6451fc5f80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f645200cc00>"}, "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": 524288, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651759426.9101102, "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": -0.04857599999999995, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 160, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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-test.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:154849fc94e89266e029d9b321f5e69de28b95bb0969d51be7cc271dfc276ed0
|
3 |
+
size 144102
|
ppo-LunarLander-test/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
ppo-LunarLander-test/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 0x7f6451fc59e0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6451fc5a70>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6451fc5b00>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6451fc5b90>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f6451fc5c20>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f6451fc5cb0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6451fc5d40>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f6451fc5dd0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6451fc5e60>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6451fc5ef0>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6451fc5f80>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f645200cc00>"
|
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": 524288,
|
46 |
+
"_total_timesteps": 500000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1651759426.9101102,
|
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": -0.04857599999999995,
|
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": 160,
|
79 |
+
"n_steps": 2048,
|
80 |
+
"gamma": 0.99,
|
81 |
+
"gae_lambda": 0.95,
|
82 |
+
"ent_coef": 0.0,
|
83 |
+
"vf_coef": 0.5,
|
84 |
+
"max_grad_norm": 0.5,
|
85 |
+
"batch_size": 64,
|
86 |
+
"n_epochs": 10,
|
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-test/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9d18b55b5bd0a241a50aeda7a8108720ccbdc88315cf373787525089dc4dcdf4
|
3 |
+
size 84893
|
ppo-LunarLander-test/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:35a49f5c2c52df864a263217c0cabb39f5c48abf9a24339a7f19a27a97b2ce57
|
3 |
+
size 43201
|
ppo-LunarLander-test/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-test/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 sha256:453db09d86e39542fbc527f1ba872f25055ac650d79c3219ff29fc1cd4c68c72
|
3 |
+
size 218340
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 220.28533785546887, "std_reward": 20.918377426726604, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-05T16:12:37.864720"}
|
test.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:800b32cfd2b15b5b41296fab38048433f081f5cd78338f036d7108d4cf428ac9
|
3 |
+
size 144102
|