Commit
•
3c40516
1
Parent(s):
763aef5
Upload PPO LunarLander-v2 trained agent v0
Browse files- .gitattributes +1 -0
- README.md +36 -0
- config.json +1 -0
- ppo_model_lunarlander_v0.zip +3 -0
- ppo_model_lunarlander_v0/_stable_baselines3_version +1 -0
- ppo_model_lunarlander_v0/data +94 -0
- ppo_model_lunarlander_v0/policy.optimizer.pth +3 -0
- ppo_model_lunarlander_v0/policy.pth +3 -0
- ppo_model_lunarlander_v0/pytorch_variables.pth +3 -0
- ppo_model_lunarlander_v0/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: 268.90 +/- 26.59
|
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 0x7fc2804794d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc280479560>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc2804795f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc280479680>", "_build": "<function ActorCriticPolicy._build at 0x7fc280479710>", "forward": "<function ActorCriticPolicy.forward at 0x7fc2804797a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc280479830>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc2804798c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc280479950>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc2804799e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc280479a70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc2804c67e0>"}, "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": 114688, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1653409942.4317389, "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": -0.1468799999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 556, "n_steps": 1024, "gamma": 0.9999, "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_model_lunarlander_v0.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8eb6208923a4dac5a3eb5a4175c95d38439d830fc4b814b4f8158f753353b663
|
3 |
+
size 144175
|
ppo_model_lunarlander_v0/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
ppo_model_lunarlander_v0/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 0x7fc2804794d0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc280479560>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc2804795f0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc280479680>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fc280479710>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fc2804797a0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc280479830>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fc2804798c0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc280479950>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc2804799e0>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc280479a70>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fc2804c67e0>"
|
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": 114688,
|
46 |
+
"_total_timesteps": 100000,
|
47 |
+
"_num_timesteps_at_start": 0,
|
48 |
+
"seed": null,
|
49 |
+
"action_noise": null,
|
50 |
+
"start_time": 1653409942.4317389,
|
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": -0.1468799999999999,
|
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": 556,
|
79 |
+
"n_steps": 1024,
|
80 |
+
"gamma": 0.9999,
|
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_model_lunarlander_v0/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:7719cd32a52b8c98deda77aa92e08d5c3f7e1dc4620fb4381e4115bad430c3d3
|
3 |
+
size 84893
|
ppo_model_lunarlander_v0/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9fa5568f0890fa542a706b43fe3782eb16d910691cd39bd76c1aa94e9347a847
|
3 |
+
size 43201
|
ppo_model_lunarlander_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_model_lunarlander_v0/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:de7f78d960943beee24e29f5eba353f2b284b4b955356da74a4ba5d17a23f08a
|
3 |
+
size 194608
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 268.9032039065732, "std_reward": 26.587120471009353, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-24T16:36:19.597169"}
|