EmberrJoel
commited on
Commit
•
9de62af
1
Parent(s):
0fb6f45
Upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -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 +0 -0
- results.json +1 -0
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
- task:
|
12 |
+
type: reinforcement-learning
|
13 |
+
name: reinforcement-learning
|
14 |
+
dataset:
|
15 |
+
name: LunarLander-v2
|
16 |
+
type: LunarLander-v2
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: 235.42 +/- 24.73
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **PPO** Agent playing **LunarLander-v2**
|
25 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
26 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
27 |
+
|
28 |
+
## Usage (with Stable-baselines3)
|
29 |
+
TODO: Add your code
|
30 |
+
|
31 |
+
|
32 |
+
```python
|
33 |
+
from stable_baselines3 import ...
|
34 |
+
from huggingface_sb3 import load_from_hub
|
35 |
+
|
36 |
+
...
|
37 |
+
```
|
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 0x7fcb9afe1160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcb9afe11f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcb9afe1280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcb9afe1310>", "_build": "<function ActorCriticPolicy._build at 0x7fcb9afe13a0>", "forward": "<function ActorCriticPolicy.forward at 0x7fcb9afe1430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcb9afe14c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcb9afe1550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcb9afe15e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcb9afe1670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcb9afe1700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcb9afdb600>"}, "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": 1670353602564424573, "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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+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 sha256:0df54d13e7075f1b2c79e43455b77ab84bf06720e61dd823769b44d9fcfee94d
|
3 |
+
size 147134
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.6.2
|
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 0x7fcb9afe1160>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcb9afe11f0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcb9afe1280>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcb9afe1310>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fcb9afe13a0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fcb9afe1430>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcb9afe14c0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fcb9afe1550>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcb9afe15e0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcb9afe1670>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcb9afe1700>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fcb9afdb600>"
|
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": 1015808,
|
46 |
+
"_total_timesteps": 1000000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1670353602564424573,
|
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 sha256:41f695e262a05116eb3cdb8957f5984ad06bf12978a7a44bf47c91627f8770d7
|
3 |
+
size 87865
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:86015fb0ab6469ebac61113c35bcce12689740a3695cd2af1b2d8baeb616920f
|
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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
|
2 |
+
Python: 3.8.15
|
3 |
+
Stable-Baselines3: 1.6.2
|
4 |
+
PyTorch: 1.12.1+cu113
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
Binary file (235 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 235.42339364783265, "std_reward": 24.725290077349552, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-06T19:31:28.163925"}
|