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 +99 -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 +9 -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: 191.03 +/- 67.93
|
| 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 0x7e9701a767a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e9701a76830>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e9701a768c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e9701a76950>", "_build": "<function ActorCriticPolicy._build at 0x7e9701a769e0>", "forward": "<function ActorCriticPolicy.forward at 0x7e9701a76a70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e9701a76b00>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e9701a76b90>", "_predict": "<function ActorCriticPolicy._predict at 0x7e9701a76c20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e9701a76cb0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e9701a76d40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e9701a76dd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e9701a11100>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1001472, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1699488211907818776, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAKA4Ir6Cpq4/5swIvxATi758V4y9tModvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0014719999999999178, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVQwwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQGXHZ75VOsWMAWyUTaECjAF0lEdAtA9knVoYenV9lChoBkdAVWDFGXokiWgHTegDaAhHQLQQyP9UCJZ1fZQoaAZHQG0vd30PH1hoB03yAWgIR0C0EaQtjCpFdX2UKGgGR0BUcKRuCPIXaAdN6ANoCEdAtBWVAkcCHXV9lChoBkdAXEa0KJEYwmgHTegDaAhHQLQXQ2FWXC11fZQoaAZHwEn9H/cWTHNoB0usaAhHQLQXg0jkdWB1fZQoaAZHQGyLH4oJAt5oB016AWgIR0C0GWSemNzbdX2UKGgGR0BYTo9X9zfaaAdN6ANoCEdAtBrIre67NHV9lChoBkdAa/eYlY2bX2gHTQMCaAhHQLQbgl8PWhB1fZQoaAZHQGrWwIUrTYxoB03YAWgIR0C0HX1dgOSXdX2UKGgGR0BsRGgvlEJCaAdNRgFoCEdAtB3ug3974XV9lChoBkdAaTyt6HCXQmgHTQwCaAhHQLQeslOoHcF1fZQoaAZHQHB13QpnYg9oB01UAWgIR0C0HyoUSIxhdX2UKGgGR0BodyBkI5YHaAdNsAJoCEdAtCAqDWbw0HV9lChoBkdAa3PwgDA8CGgHTZIBaAhHQLQizRHww0x1fZQoaAZHQGm+lx4ptrNoB01gAWgIR0C0I4a2jO9ndX2UKGgGR0BphwZydWhiaAdNRQFoCEdAtCQ0RODaoXV9lChoBkdAal0Zx7zClGgHTe0BaAhHQLQlOFPSDyx1fZQoaAZHQGYnTZxrBTJoB02GAmgIR0C0J32Z/kNndX2UKGgGR0Bty1cbBGhFaAdNbQFoCEdAtCgAp8WsR3V9lChoBkdAazGDYh+vyWgHTYABaAhHQLQohXm/3391fZQoaAZHQGtlsGX5WR1oB03SAmgIR0C0KYNg4OtodX2UKGgGR0BsUyzNUwSKaAdNsAJoCEdAtCvVshxHXnV9lChoBkdAbA+mnfl6q2gHTS0CaAhHQLQsl+Vkc0d1fZQoaAZHwCafk3juKGdoB01SAWgIR0C0LQ/omoitdX2UKGgGR0BaqiaAnUlSaAdN6ANoCEdAtDAbeqJdjXV9lChoBkdAaom7z06HTWgHTbECaAhHQLQxaSuhbnp1fZQoaAZHQGmChMSK3uxoB03KAWgIR0C0MmCJCSiedX2UKGgGR0BqtCnJkoWpaAdNTQJoCEdAtDOg/JNj9XV9lChoBkdAaUAQ7tAs1GgHTYgBaAhHQLQ1w1+y7f51fZQoaAZHwEoHP4VRDTloB00yAWgIR0C0Ni86JZW8dX2UKGgGR0BrpYMvysjnaAdNIwJoCEdAtDbxeokzGnV9lChoBkdAaxfIJ7b+LmgHTUgBaAhHQLQ3ZmCyyD91fZQoaAZHQGpQwG4ZuQ9oB020AWgIR0C0N/6xLTQWdX2UKGgGR0Bn0+s1baAXaAdNTwJoCEdAtDopZmqYJHV9lChoBkdAaWlXMhX8wmgHTd8BaAhHQLQ61Zsbedl1fZQoaAZHQFDCn8sMAm1oB03oA2gIR0C0PDy2QXANdX2UKGgGR0BpCoYrJ8v3aAdNvgJoCEdAtD6qsA/9pHV9lChoBkdAVGeNEPUaymgHTegDaAhHQLRAiSDyvs91fZQoaAZHQG2BA2ycCo1oB025AWgIR0C0QW/a6BiDdX2UKGgGR0BspEWhysCDaAdN3gFoCEdAtEQSax5cDHV9lChoBkdAZ0EW0JF9a2gHTbIBaAhHQLREq8VHnU51fZQoaAZHQGLsE1dgOSZoB037AWgIR0C0RWBllK9PdX2UKGgGR0Bq9OHrQgLaaAdNbQJoCEdAtEY9WQwK0HV9lChoBkfAQVNZTyauwGgHTSEBaAhHQLRIAFspG4J1fZQoaAZHQGwj01ZTyaxoB03OAWgIR0C0SKYiHIp6dX2UKGgGR0BqBAKSgXdkaAdNAwJoCEdAtElf94u9OHV9lChoBkdAap1Ynv2GqWgHTTMCaAhHQLRKKihWYF91fZQoaAZHQGtr+wC8vmJoB00wAmgIR0C0TEVMZgogdX2UKGgGR0BYWIMOPNmlaAdN6ANoCEdAtE3IGMXJo3V9lChoBkdAaRVAFgUlA2gHTUQDaAhHQLRPUwbVBld1fZQoaAZHQGp0W4mTkhloB021AmgIR0C0UrdMfzSUdX2UKGgGR0BdkS31BdD6aAdN6ANoCEdAtFQfJA+pwXV9lChoBkdAWVYFzMibD2gHTegDaAhHQLRW6NQTEit1fZQoaAZHwBZ+kDZDiOxoB00gAWgIR0C0V08TBZZCdX2UKGgGR8AxO6Zpi7TVaAdNVgFoCEdAtFfJeruIAXV9lChoBkdAbDbEzfrKNmgHTYMBaAhHQLRYWKraM751fZQoaAZHwD2uCmMwUQFoB002AWgIR0C0WMclPacqdX2UKGgGR0Brtp+SbH6uaAdNswJoCEdAtFskPYnOSnV9lChoBkdAa7AsPJ7swGgHTR4CaAhHQLRb5lzU7S11fZQoaAZHQGpjHJ1aGHpoB02iAWgIR0C0XHt70Fr3dX2UKGgGR8BHnzbnHNoraAdNRwFoCEdAtF0ROIqLCXV9lChoBkdAafXuIAOrhmgHTcQBaAhHQLRf3VOsT391fZQoaAZHQDtAeo1k1/FoB00ZAWgIR0C0YHTbN8mbdX2UKGgGR8BPMeQuEmICaAdNEQFoCEdAtGEECGN70HV9lChoBkdAaSsGu9vjwWgHTfEBaAhHQLRh9bEP1+R1fZQoaAZHQGxgoAXEZR9oB03nAWgIR0C0YqalchTwdX2UKGgGR0BZzaySmqHXaAdN6ANoCEdAtGVut8uzyHV9lChoBkdAaGhn0TURWmgHTZgBaAhHQLRl++zMRpV1fZQoaAZHQGcCXn6l+E1oB01DAWgIR0C0Zm6ujh1ldX2UKGgGR0BqWy7I1cdHaAdNIwJoCEdAtGcpHTZxrHV9lChoBkdAbQ8UTL4etGgHTQgCaAhHQLRpN/NJOFh1fZQoaAZHQGSdGPYFqztoB03mA2gIR0C0ap9Q9A5adX2UKGgGR0Bp79Pva11GaAdNLQJoCEdAtGthw0fozXV9lChoBkdAE25myxA0K2gHTRgBaAhHQLRtlh2GIsR1fZQoaAZHwDxLZSNwR5FoB01JAWgIR0C0bj94iX6ZdX2UKGgGR0BtmL8xbjcVaAdNyQFoCEdAtG83+CK77XV9lChoBkdAaFRKGL1mJ2gHTbYBaAhHQLRwJG0/nnx1fZQoaAZHQGue7p/wy7BoB01UAmgIR0C0cTy8SPELdX2UKGgGR8BNGjOs1baAaAdNLAFoCEdAtHL1tgrpaHV9lChoBkdAakT/n4fwJGgHTdcBaAhHQLRznEYfnwJ1fZQoaAZHwDO/hHbypaRoB00VAWgIR0C0dAAYpDu0dX2UKGgGR0BuU4n+hoM8aAdNaAFoCEdAtHSAOhCdBnV9lChoBkdAZsKgieNDMWgHTQ8DaAhHQLR28VEd/8V1fZQoaAZHQGuQN/WlMytoB02aAWgIR0C0d32cSXdCdX2UKGgGR0BpdvRRdhRZaAdNqAFoCEdAtHgQmkWRBHV9lChoBkdAa+un7YTTOWgHTSoCaAhHQLR411ie/Yd1fZQoaAZHQGtgiAtnPE9oB03AAmgIR0C0e0/95yEMdX2UKGgGR0BrK31nM+vAaAdNtQFoCEdAtHwbLdN34nV9lChoBkdAbivPw/gR9WgHTZ8BaAhHQLR85awUxmF1fZQoaAZHQG5ouOS4e91oB01uAWgIR0C0fadSMtK7dX2UKGgGR0BrZ3MGHHmzaAdNJgJoCEdAtH7HkcS5AnV9lChoBkdAassxC6YmcGgHTZEBaAhHQLSBCq1gH/t1fZQoaAZHQGxxkMspXp5oB01bAmgIR0C0geJKaodddX2UKGgGR0BsK8YAKfFraAdNJgJoCEdAtIKnvRZ2ZHV9lChoBkdAa4SZsKsuF2gHTeABaAhHQLSDVbKifxt1fZQoaAZHQGfDPwmVqvhoB036AWgIR0C0hWAw9JSSdX2UKGgGR0BsGyXyAhB7aAdNiQFoCEdAtIXpUEPlMnV9lChoBkdAassSPEKmbmgHTUECaAhHQLSGskuHvc91fZQoaAZHQGwp/IS13MZoB02NAmgIR0C0h5k/0NBodWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 9780, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
ppo-LunarLander-v2.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0a046131709f45db896c15b877891c2f2a06e278b1444868584b4f466ec99ef3
|
| 3 |
+
size 147394
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
2.0.0a5
|
ppo-LunarLander-v2/data
ADDED
|
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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 0x7e9701a767a0>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e9701a76830>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e9701a768c0>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e9701a76950>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7e9701a769e0>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7e9701a76a70>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7e9701a76b00>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e9701a76b90>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7e9701a76c20>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e9701a76cb0>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e9701a76d40>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7e9701a76dd0>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7e9701a11100>"
|
| 21 |
+
},
|
| 22 |
+
"verbose": 1,
|
| 23 |
+
"policy_kwargs": {},
|
| 24 |
+
"num_timesteps": 1001472,
|
| 25 |
+
"_total_timesteps": 1000000,
|
| 26 |
+
"_num_timesteps_at_start": 0,
|
| 27 |
+
"seed": null,
|
| 28 |
+
"action_noise": null,
|
| 29 |
+
"start_time": 1699488211907818776,
|
| 30 |
+
"learning_rate": 0.0003,
|
| 31 |
+
"tensorboard_log": null,
|
| 32 |
+
"_last_obs": {
|
| 33 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 34 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAKA4Ir6Cpq4/5swIvxATi758V4y9tModvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
|
| 35 |
+
},
|
| 36 |
+
"_last_episode_starts": {
|
| 37 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 38 |
+
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
|
| 39 |
+
},
|
| 40 |
+
"_last_original_obs": null,
|
| 41 |
+
"_episode_num": 0,
|
| 42 |
+
"use_sde": false,
|
| 43 |
+
"sde_sample_freq": -1,
|
| 44 |
+
"_current_progress_remaining": -0.0014719999999999178,
|
| 45 |
+
"_stats_window_size": 100,
|
| 46 |
+
"ep_info_buffer": {
|
| 47 |
+
":type:": "<class 'collections.deque'>",
|
| 48 |
+
":serialized:": "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"
|
| 49 |
+
},
|
| 50 |
+
"ep_success_buffer": {
|
| 51 |
+
":type:": "<class 'collections.deque'>",
|
| 52 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 53 |
+
},
|
| 54 |
+
"_n_updates": 9780,
|
| 55 |
+
"observation_space": {
|
| 56 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
| 57 |
+
":serialized:": "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",
|
| 58 |
+
"dtype": "float32",
|
| 59 |
+
"bounded_below": "[ True True True True True True True True]",
|
| 60 |
+
"bounded_above": "[ True True True True True True True True]",
|
| 61 |
+
"_shape": [
|
| 62 |
+
8
|
| 63 |
+
],
|
| 64 |
+
"low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
| 65 |
+
"high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
| 66 |
+
"low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
| 67 |
+
"high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
| 68 |
+
"_np_random": null
|
| 69 |
+
},
|
| 70 |
+
"action_space": {
|
| 71 |
+
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
| 72 |
+
":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
|
| 73 |
+
"n": "4",
|
| 74 |
+
"start": "0",
|
| 75 |
+
"_shape": [],
|
| 76 |
+
"dtype": "int64",
|
| 77 |
+
"_np_random": null
|
| 78 |
+
},
|
| 79 |
+
"n_envs": 1,
|
| 80 |
+
"n_steps": 2048,
|
| 81 |
+
"gamma": 0.99,
|
| 82 |
+
"gae_lambda": 0.95,
|
| 83 |
+
"ent_coef": 0.0,
|
| 84 |
+
"vf_coef": 0.5,
|
| 85 |
+
"max_grad_norm": 0.5,
|
| 86 |
+
"batch_size": 64,
|
| 87 |
+
"n_epochs": 10,
|
| 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 |
+
"lr_schedule": {
|
| 96 |
+
":type:": "<class 'function'>",
|
| 97 |
+
":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
|
| 98 |
+
}
|
| 99 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f932c34a17745d1e98c1e413c35c106d6dd3a2d542b70f6a92b54490e25c36d1
|
| 3 |
+
size 88362
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:888b0a7ff053dd375886dc5a75eef41eaa3ce9d495bb915c34e768f69a6243a7
|
| 3 |
+
size 43762
|
ppo-LunarLander-v2/pytorch_variables.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
|
| 3 |
+
size 864
|
ppo-LunarLander-v2/system_info.txt
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
|
| 2 |
+
- Python: 3.10.12
|
| 3 |
+
- Stable-Baselines3: 2.0.0a5
|
| 4 |
+
- PyTorch: 2.1.0+cu118
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
+
- Numpy: 1.23.5
|
| 7 |
+
- Cloudpickle: 2.2.1
|
| 8 |
+
- Gymnasium: 0.28.1
|
| 9 |
+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
|
Binary file (163 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 191.03225859516436, "std_reward": 67.92574819246079, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-09T00:58:16.936878"}
|