DwightGunning
commited on
Commit
•
265cc08
1
Parent(s):
8754772
Train LunarLander agent for first time
Browse files- .gitattributes +1 -0
- README.md +36 -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 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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: 279.17 +/- 18.21
|
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**
|
25 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
26 |
+
|
27 |
+
## Usage (with Stable-baselines3)
|
28 |
+
TODO: Add your code
|
29 |
+
|
30 |
+
|
31 |
+
```python
|
32 |
+
from stable_baselines3 import ...
|
33 |
+
from huggingface_sb3 import load_from_hub
|
34 |
+
|
35 |
+
...
|
36 |
+
```
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7fef5dfff680>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fef5dfff710>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fef5dfff7a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fef5dfff830>", "_build": "<function ActorCriticPolicy._build at 0x7fef5dfff8c0>", "forward": "<function ActorCriticPolicy.forward at 0x7fef5dfff950>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fef5dfff9e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fef5dfffa70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fef5dfffb00>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fef5dfffb90>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fef5dfffc20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fef5dfcbab0>"}, "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1212416, "_total_timesteps": 1200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1653332729.3476493, "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.010346666666666726, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 420, "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:bf9ab7a288486182f2f722724abcdef97ff5728fa9010992006ef023b34cf795
|
3 |
+
size 144134
|
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:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
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 0x7fef5dfff680>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fef5dfff710>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fef5dfff7a0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fef5dfff830>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fef5dfff8c0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fef5dfff950>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fef5dfff9e0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fef5dfffa70>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fef5dfffb00>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fef5dfffb90>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fef5dfffc20>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fef5dfcbab0>"
|
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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
39 |
+
"n": 4,
|
40 |
+
"_shape": [],
|
41 |
+
"dtype": "int64",
|
42 |
+
"_np_random": null
|
43 |
+
},
|
44 |
+
"n_envs": 16,
|
45 |
+
"num_timesteps": 1212416,
|
46 |
+
"_total_timesteps": 1200000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1653332729.3476493,
|
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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
|
64 |
+
},
|
65 |
+
"_last_original_obs": null,
|
66 |
+
"_episode_num": 0,
|
67 |
+
"use_sde": false,
|
68 |
+
"sde_sample_freq": -1,
|
69 |
+
"_current_progress_remaining": -0.010346666666666726,
|
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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
77 |
+
},
|
78 |
+
"_n_updates": 420,
|
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:fd1b5b4f3163bdfec34518bb84c4b767d19a3a7ec6a040552f9df954274e22e8
|
3 |
+
size 84893
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:14b01bc9fe957d5d73b377593fd03a4f7fb6c59697d2719fa0970db5460daf45
|
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 sha256:59b8e7a185ea5426b048db8ece1d91956e678a98537d23a69eac8fbafdb6e754
|
3 |
+
size 217408
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 279.1722165349859, "std_reward": 18.214316328441555, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-23T19:40:42.885438"}
|