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: 260.39 +/- 17.91
|
| 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 0x7f0a737dc9d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0a737dca60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0a737dcaf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0a737dcb80>", "_build": "<function ActorCriticPolicy._build at 0x7f0a737dcc10>", "forward": "<function ActorCriticPolicy.forward at 0x7f0a737dcca0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0a737dcd30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0a737dcdc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f0a737dce50>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0a737dcee0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0a737dcf70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0a737e0040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f0a737de060>"}, "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": 1674159974914847320, "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:d81c5606701053ee13adf1c5ba298e8a79d1f7ff68e963a8a9f64afb3aef88b3
|
| 3 |
+
size 147388
|
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 0x7f0a737dc9d0>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0a737dca60>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0a737dcaf0>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0a737dcb80>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f0a737dcc10>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f0a737dcca0>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0a737dcd30>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0a737dcdc0>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f0a737dce50>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0a737dcee0>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0a737dcf70>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0a737e0040>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc_data object at 0x7f0a737de060>"
|
| 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": 1674159974914847320,
|
| 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 sha256:20b0e5ad7fb917cd843d329d26d9a1aaec0b1e94ffc2f1a7cbb757621f1214da
|
| 3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6da7bfdb37d775ab5b1aea24024fb715f7bcf6b3bd9a4200912cb940dc13af6c
|
| 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 (226 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 260.38555628884217, "std_reward": 17.906929857791038, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-19T20:45:21.348697"}
|