Initial Commit
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: 233.78 +/- 19.45
|
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 0x7fc1d67a2a70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc1d67a2b00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc1d67a2b90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc1d67a2c20>", "_build": "<function ActorCriticPolicy._build at 0x7fc1d67a2cb0>", "forward": "<function ActorCriticPolicy.forward at 0x7fc1d67a2d40>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc1d67a2dd0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc1d67a2e60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc1d67a2ef0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc1d67a2f80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc1d67a6050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc1d67e6c00>"}, "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": 1651706105.7379718, "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-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:59cf7b1b93169e253ef57759384bbf546b864b51eea20d7e25396b2e7224cf30
|
3 |
+
size 144110
|
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 0x7fc1d67a2a70>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc1d67a2b00>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc1d67a2b90>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc1d67a2c20>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fc1d67a2cb0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fc1d67a2d40>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc1d67a2dd0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fc1d67a2e60>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc1d67a2ef0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc1d67a2f80>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc1d67a6050>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fc1d67e6c00>"
|
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": 1651706105.7379718,
|
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-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f061358a6f54c0843581a0425ff31f1677e4c6c77d5b4fef038ab0b611a4c31d
|
3 |
+
size 84893
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e415f7d520bb3edaa5ec73f0990a7b8d27a7c2b0d6039f45af09267effde31a1
|
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:a7088c4cefa3e1242f6cba2b98cc1c08c51a5dcb42fde63953c8cf7450bd5d18
|
3 |
+
size 246326
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 233.7828864262523, "std_reward": 19.45242124300442, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-04T23:44:15.399388"}
|