kumarkanth218
commited on
Commit
·
0234554
1
Parent(s):
628fbac
upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2-rupesh.zip +3 -0
- ppo-LunarLander-v2-rupesh/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2-rupesh/data +94 -0
- ppo-LunarLander-v2-rupesh/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2-rupesh/policy.pth +3 -0
- ppo-LunarLander-v2-rupesh/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2-rupesh/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: 275.99 +/- 13.56
|
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 0x7fd0aea93040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd0aea930d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd0aea93160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd0aea931f0>", "_build": "<function ActorCriticPolicy._build at 0x7fd0aea93280>", "forward": "<function ActorCriticPolicy.forward at 0x7fd0aea93310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd0aea933a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd0aea93430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd0aea934c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd0aea93550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd0aea935e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd0aea8f4b0>"}, "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": 1673161979250505464, "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-5.10.147+-x86_64-with-glibc2.27 #1 SMP Sat Dec 10 16:00:40 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-rupesh.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:10fa348b4e85dcc9a09bb7eb99f26270a61f55733803c70381b8ebecad64f739
|
3 |
+
size 147198
|
ppo-LunarLander-v2-rupesh/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.6.2
|
ppo-LunarLander-v2-rupesh/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 0x7fd0aea93040>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd0aea930d0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd0aea93160>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd0aea931f0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fd0aea93280>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fd0aea93310>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd0aea933a0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fd0aea93430>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd0aea934c0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd0aea93550>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd0aea935e0>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fd0aea8f4b0>"
|
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": 1673161979250505464,
|
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-rupesh/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:aebe642083dddff7a545440d091665717fb41c6642fb471854df55c893dc3ca9
|
3 |
+
size 87929
|
ppo-LunarLander-v2-rupesh/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:484ba6f837863ae0a9b6d742f23ee4c366d1686a0764d9c9438b2fb7f74463de
|
3 |
+
size 43201
|
ppo-LunarLander-v2-rupesh/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-rupesh/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
OS: Linux-5.10.147+-x86_64-with-glibc2.27 #1 SMP Sat Dec 10 16:00:40 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 (244 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 275.9921785917972, "std_reward": 13.557172554740406, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-08T07:45:29.395337"}
|