Upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +95 -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 +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: 255.69 +/- 13.27
|
| 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 0x7f1c75f6fdc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1c75f6fe50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1c75f6fee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1c75f6ff70>", "_build": "<function ActorCriticPolicy._build at 0x7f1c75f73040>", "forward": "<function ActorCriticPolicy.forward at 0x7f1c75f730d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f1c75f73160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1c75f731f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1c75f73280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1c75f73310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1c75f733a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1c75f73430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1c75fd9c90>"}, "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": 1677078506277213007, "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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "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 sha256:82ddce6df6cb8411874ae5ed1c9b9a5a9339592cb7aa52ed996cac10829882d4
|
| 3 |
+
size 147420
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.7.0
|
ppo-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 0x7f1c75f6fdc0>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1c75f6fe50>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1c75f6fee0>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1c75f6ff70>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f1c75f73040>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f1c75f730d0>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f1c75f73160>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1c75f731f0>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f1c75f73280>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1c75f73310>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1c75f733a0>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1c75f73430>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc_data object at 0x7f1c75fd9c90>"
|
| 21 |
+
},
|
| 22 |
+
"verbose": 1,
|
| 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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
| 40 |
+
"n": 4,
|
| 41 |
+
"_shape": [],
|
| 42 |
+
"dtype": "int64",
|
| 43 |
+
"_np_random": null
|
| 44 |
+
},
|
| 45 |
+
"n_envs": 16,
|
| 46 |
+
"num_timesteps": 1015808,
|
| 47 |
+
"_total_timesteps": 1000000,
|
| 48 |
+
"_num_timesteps_at_start": 0,
|
| 49 |
+
"seed": null,
|
| 50 |
+
"action_noise": null,
|
| 51 |
+
"start_time": 1677078506277213007,
|
| 52 |
+
"learning_rate": 0.0003,
|
| 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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
| 65 |
+
},
|
| 66 |
+
"_last_original_obs": null,
|
| 67 |
+
"_episode_num": 0,
|
| 68 |
+
"use_sde": false,
|
| 69 |
+
"sde_sample_freq": -1,
|
| 70 |
+
"_current_progress_remaining": -0.015808000000000044,
|
| 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": 248,
|
| 80 |
+
"n_steps": 1024,
|
| 81 |
+
"gamma": 0.999,
|
| 82 |
+
"gae_lambda": 0.98,
|
| 83 |
+
"ent_coef": 0.01,
|
| 84 |
+
"vf_coef": 0.5,
|
| 85 |
+
"max_grad_norm": 0.5,
|
| 86 |
+
"batch_size": 64,
|
| 87 |
+
"n_epochs": 4,
|
| 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 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9a8f3f497e1559ea57492378bf3f10179b4daff741e9310c15b3b58a86a79b35
|
| 3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c75d69380ec523ba38d3b8493d8c81737a336e8a46ddfc74d5c77c0b2d0dfb27
|
| 3 |
+
size 43393
|
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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
| 2 |
+
- Python: 3.8.10
|
| 3 |
+
- Stable-Baselines3: 1.7.0
|
| 4 |
+
- PyTorch: 1.13.1+cu116
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
+
- Numpy: 1.21.6
|
| 7 |
+
- Gym: 0.21.0
|
replay.mp4
ADDED
|
Binary file (227 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 255.6914326652326, "std_reward": 13.26804876278819, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-22T15:38:42.794260"}
|