Upload PPO LunarLander-v2 agent trained for 500000 timesteps
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: 121.87 +/- 96.84
|
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 0x7f9460ab3950>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9460ab39e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9460ab3a70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9460ab3b00>", "_build": "<function ActorCriticPolicy._build at 0x7f9460ab3b90>", "forward": "<function ActorCriticPolicy.forward at 0x7f9460ab3c20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9460ab3cb0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9460ab3d40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9460ab3dd0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9460ab3e60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9460ab3ef0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9460affba0>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652266835.3291311, "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 sha256:73c13f52c94dbe194806d01a7ad0edd9f15f96db8a661e0c3af26cde46c3e631
|
3 |
+
size 144036
|
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 0x7f9460ab3950>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9460ab39e0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9460ab3a70>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9460ab3b00>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f9460ab3b90>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f9460ab3c20>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9460ab3cb0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f9460ab3d40>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9460ab3dd0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9460ab3e60>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9460ab3ef0>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f9460affba0>"
|
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": 1652266835.3291311,
|
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 sha256:57d5b5cfe33d6b3b39948813125ffbf80680212f9c0687cd2998a8e0d5701641
|
3 |
+
size 84829
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1514c62466848adae92f174b58a783f2fb86bfad91f721b9bb179829754a50f2
|
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 sha256:93fbba09e9c1cb6609411453f08fa8ea15c276465e556d9dd04ff60db5596de9
|
3 |
+
size 205316
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 121.86650565896196, "std_reward": 96.83782917731901, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-11T11:20:46.071480"}
|