first commit
Browse files- README.md +36 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +94 -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 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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: 298.08 +/- 18.36
|
| 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 0x7f9d44b80200>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9d44b80290>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9d44b80320>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9d44b803b0>", "_build": "<function ActorCriticPolicy._build at 0x7f9d44b80440>", "forward": "<function ActorCriticPolicy.forward at 0x7f9d44b804d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9d44b80560>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9d44b805f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9d44b80680>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9d44b80710>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9d44b807a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9d44bd6210>"}, "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": 5013504, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1657573358.0613685, "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": -0.0027007999999999477, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1348, "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-5.4.188+-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-LunarLander-v2.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:92042c3fc6eb1d676b6fd6fd4f78e7de992550e739cadf9dff654aa9a40eede3
|
| 3 |
+
size 144096
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.5.0
|
ppo-LunarLander-v2/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 0x7f9d44b80200>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9d44b80290>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9d44b80320>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9d44b803b0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f9d44b80440>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f9d44b804d0>",
|
| 13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9d44b80560>",
|
| 14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f9d44b805f0>",
|
| 15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9d44b80680>",
|
| 16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9d44b80710>",
|
| 17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9d44b807a0>",
|
| 18 |
+
"__abstractmethods__": "frozenset()",
|
| 19 |
+
"_abc_impl": "<_abc_data object at 0x7f9d44bd6210>"
|
| 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": 5013504,
|
| 46 |
+
"_total_timesteps": 5000000,
|
| 47 |
+
"_num_timesteps_at_start": 0,
|
| 48 |
+
"seed": null,
|
| 49 |
+
"action_noise": null,
|
| 50 |
+
"start_time": 1657573358.0613685,
|
| 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": -0.0027007999999999477,
|
| 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": 1348,
|
| 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/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:995abf6e33b4f3572540beff400b98002d923cf4122875ba72f94102e4f609a4
|
| 3 |
+
size 84893
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4908932513e5c4d620ae678efa9d67ae0581e87dc40e88c8dd1c13c1c6d90c12
|
| 3 |
+
size 43201
|
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-5.4.188+-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
|
Binary file (181 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 298.0785933273079, "std_reward": 18.36482293130718, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-11T22:09:19.206406"}
|