This is another Reinforsement Learning model I made via HuggingFace's course
Browse files- LunarLander-v2.zip +3 -0
- LunarLander-v2/_stable_baselines3_version +1 -0
- LunarLander-v2/data +95 -0
- LunarLander-v2/policy.optimizer.pth +3 -0
- LunarLander-v2/policy.pth +3 -0
- LunarLander-v2/pytorch_variables.pth +3 -0
- LunarLander-v2/system_info.txt +7 -0
- README.md +37 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:35e10f5ef11b0bb030ffec5c3a732c88af1a18ee1b67df5b1f7548a7a3a25951
|
3 |
+
size 149531
|
LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.7.0
|
LunarLander-v2/data
ADDED
@@ -0,0 +1,95 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7fe091b04ca0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe091b04d30>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe091b04dc0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe091b04e50>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fe091b04ee0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fe091b04f70>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe091b09040>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe091b090d0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fe091b09160>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe091b091f0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe091b09280>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe091b09310>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc_data object at 0x7fe091b008d0>"
|
21 |
+
},
|
22 |
+
"verbose": false,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"observation_space": {
|
25 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
26 |
+
":serialized:": "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",
|
27 |
+
"dtype": "float32",
|
28 |
+
"_shape": [
|
29 |
+
8
|
30 |
+
],
|
31 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
32 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
33 |
+
"bounded_below": "[False False False False False False False False]",
|
34 |
+
"bounded_above": "[False False False False False False False False]",
|
35 |
+
"_np_random": null
|
36 |
+
},
|
37 |
+
"action_space": {
|
38 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
39 |
+
":serialized:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
40 |
+
"n": 4,
|
41 |
+
"_shape": [],
|
42 |
+
"dtype": "int64",
|
43 |
+
"_np_random": null
|
44 |
+
},
|
45 |
+
"n_envs": 64,
|
46 |
+
"num_timesteps": 20054016,
|
47 |
+
"_total_timesteps": 20000000,
|
48 |
+
"_num_timesteps_at_start": 0,
|
49 |
+
"seed": null,
|
50 |
+
"action_noise": null,
|
51 |
+
"start_time": 1677210043316520647,
|
52 |
+
"learning_rate": 0.0001,
|
53 |
+
"tensorboard_log": null,
|
54 |
+
"lr_schedule": {
|
55 |
+
":type:": "<class 'function'>",
|
56 |
+
":serialized:": "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"
|
57 |
+
},
|
58 |
+
"_last_obs": {
|
59 |
+
":type:": "<class 'numpy.ndarray'>",
|
60 |
+
":serialized:": "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"
|
61 |
+
},
|
62 |
+
"_last_episode_starts": {
|
63 |
+
":type:": "<class 'numpy.ndarray'>",
|
64 |
+
":serialized:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="
|
65 |
+
},
|
66 |
+
"_last_original_obs": null,
|
67 |
+
"_episode_num": 0,
|
68 |
+
"use_sde": false,
|
69 |
+
"sde_sample_freq": -1,
|
70 |
+
"_current_progress_remaining": -0.0027007999999999477,
|
71 |
+
"ep_info_buffer": {
|
72 |
+
":type:": "<class 'collections.deque'>",
|
73 |
+
":serialized:": "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"
|
74 |
+
},
|
75 |
+
"ep_success_buffer": {
|
76 |
+
":type:": "<class 'collections.deque'>",
|
77 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
78 |
+
},
|
79 |
+
"_n_updates": 2448,
|
80 |
+
"n_steps": 1024,
|
81 |
+
"gamma": 0.999,
|
82 |
+
"gae_lambda": 0.96,
|
83 |
+
"ent_coef": 0.0,
|
84 |
+
"vf_coef": 0.5,
|
85 |
+
"max_grad_norm": 0.5,
|
86 |
+
"batch_size": 128,
|
87 |
+
"n_epochs": 8,
|
88 |
+
"clip_range": {
|
89 |
+
":type:": "<class 'function'>",
|
90 |
+
":serialized:": "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"
|
91 |
+
},
|
92 |
+
"clip_range_vf": null,
|
93 |
+
"normalize_advantage": true,
|
94 |
+
"target_kl": null
|
95 |
+
}
|
LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:318454685c62f4fad0220438308fcea2a81cdd6a9aa051459512dd6a46562da3
|
3 |
+
size 87929
|
LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:35b2cd0294f8a5277e701ba4c7296b46eb866eb896c76b4d887bddb86bf73bb1
|
3 |
+
size 43393
|
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
|
LunarLander-v2/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-5.15.0-58-generic-x86_64-with-glibc2.17 # 64-Ubuntu SMP Thu Jan 5 11:43:13 UTC 2023
|
2 |
+
- Python: 3.8.16
|
3 |
+
- Stable-Baselines3: 1.7.0
|
4 |
+
- PyTorch: 1.13.1+cu117
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.24.2
|
7 |
+
- Gym: 0.21.0
|
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: 292.55 +/- 16.62
|
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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7fe091b04ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe091b04d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe091b04dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe091b04e50>", "_build": "<function ActorCriticPolicy._build at 0x7fe091b04ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7fe091b04f70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe091b09040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe091b090d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe091b09160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe091b091f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe091b09280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe091b09310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe091b008d0>"}, "verbose": false, "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 64, "num_timesteps": 20054016, "_total_timesteps": 20000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677210043316520647, "learning_rate": 0.0001, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWV5QIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMWS9ob21lL3JsL21pbmljb25kYTMvZW52cy9ybC9saWIvcHl0aG9uMy44L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgkMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxZL2hvbWUvcmwvbWluaWNvbmRhMy9lbnZzL3JsL2xpYi9weXRob24zLjgvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPxo24uscQy2FlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0027007999999999477, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 2448, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.96, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.15.0-58-generic-x86_64-with-glibc2.17 # 64-Ubuntu SMP Thu Jan 5 11:43:13 UTC 2023", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.2", "Gym": "0.21.0"}}
|
replay.mp4
ADDED
Binary file (219 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 292.5546617904278, "std_reward": 16.617023604498144, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-24T04:58:34.284018"}
|