Commit
·
46a01fc
1
Parent(s):
ccf34cc
Upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- model-1.zip +3 -0
- model-1/_stable_baselines3_version +1 -0
- model-1/data +94 -0
- model-1/policy.optimizer.pth +3 -0
- model-1/policy.pth +3 -0
- model-1/pytorch_variables.pth +3 -0
- model-1/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: 232.76 +/- 59.75
|
| 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 0x7f3724d6b310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3724d6b3a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3724d6b430>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3724d6b4c0>", "_build": "<function ActorCriticPolicy._build at 0x7f3724d6b550>", "forward": "<function ActorCriticPolicy.forward at 0x7f3724d6b5e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3724d6b670>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3724d6b700>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3724d6b790>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3724d6b820>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3724d6b8b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f3724d6a150>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 1672315575019266296, "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": 310, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 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"}}
|
model-1.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:32c0c54aabc9b91a47c4789fdf4b7fed66482f192a009ce3babd612a1f1fdd47
|
| 3 |
+
size 147153
|
model-1/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.6.2
|
model-1/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 0x7f3724d6b310>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3724d6b3a0>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3724d6b430>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3724d6b4c0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f3724d6b550>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f3724d6b5e0>",
|
| 13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3724d6b670>",
|
| 14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f3724d6b700>",
|
| 15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3724d6b790>",
|
| 16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3724d6b820>",
|
| 17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3724d6b8b0>",
|
| 18 |
+
"__abstractmethods__": "frozenset()",
|
| 19 |
+
"_abc_impl": "<_abc_data object at 0x7f3724d6a150>"
|
| 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": 1672315575019266296,
|
| 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": 310,
|
| 79 |
+
"n_steps": 2048,
|
| 80 |
+
"gamma": 0.99,
|
| 81 |
+
"gae_lambda": 0.95,
|
| 82 |
+
"ent_coef": 0.0,
|
| 83 |
+
"vf_coef": 0.5,
|
| 84 |
+
"max_grad_norm": 0.5,
|
| 85 |
+
"batch_size": 64,
|
| 86 |
+
"n_epochs": 10,
|
| 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 |
+
}
|
model-1/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7ea64dbfe4c5b80e167b14874a664ee994a0d90012f0c6c49b78e1ace93563b1
|
| 3 |
+
size 87929
|
model-1/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4510d4c41af1d342fe0839b5ac7356b0caa00ef6b4f9a7f3e89feac9b6cdab75
|
| 3 |
+
size 43201
|
model-1/pytorch_variables.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
| 3 |
+
size 431
|
model-1/system_info.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 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 (236 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 232.7574745958987, "std_reward": 59.746179132246176, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-29T12:34:15.731184"}
|