First try on training the lunar lander
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2_v0.zip +3 -0
- ppo-LunarLander-v2_v0/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2_v0/data +94 -0
- ppo-LunarLander-v2_v0/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2_v0/policy.pth +3 -0
- ppo-LunarLander-v2_v0/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2_v0/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: 286.53 +/- 20.43
|
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 0x7f7f264eb5e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7f264eb670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7f264eb700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7f264eb790>", "_build": "<function ActorCriticPolicy._build at 0x7f7f264eb820>", "forward": "<function ActorCriticPolicy.forward at 0x7f7f264eb8b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7f264eb940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7f264eb9d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7f264eba60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7f264ebaf0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7f264ebb80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7f26562e40>"}, "verbose": 0, "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": 1670500741217993136, "learning_rate": 0.003, "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": 355, "n_steps": 1024, "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": 5, "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": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2_v0.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1de41efae18310301581fa9ef5dee7a6d4c6dc6b04b69442045a2036992c2f97
|
3 |
+
size 147215
|
ppo-LunarLander-v2_v0/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.6.2
|
ppo-LunarLander-v2_v0/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 0x7f7f264eb5e0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7f264eb670>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7f264eb700>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7f264eb790>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f7f264eb820>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f7f264eb8b0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7f264eb940>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f7f264eb9d0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7f264eba60>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7f264ebaf0>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7f264ebb80>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f7f26562e40>"
|
20 |
+
},
|
21 |
+
"verbose": 0,
|
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": 1670500741217993136,
|
51 |
+
"learning_rate": 0.003,
|
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": 355,
|
79 |
+
"n_steps": 1024,
|
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": 5,
|
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_v0/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:7f5dbbca83d1704fc47f042ecc541fe304c2114575d270f75aef505a51758cc5
|
3 |
+
size 88057
|
ppo-LunarLander-v2_v0/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:195a642523dcc137206e559e283dbcc8fcd4043d376a5a5786f99de2bf5e8998
|
3 |
+
size 43201
|
ppo-LunarLander-v2_v0/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_v0/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: 3.8.16
|
3 |
+
Stable-Baselines3: 1.6.2
|
4 |
+
PyTorch: 1.13.0+cu116
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
Binary file (230 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 286.53253925602695, "std_reward": 20.434188859433114, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-10T10:17:06.308914"}
|