PPO LunarLander-v2 trained agent 1M steps
Browse files- README.md +15 -8
- config.json +1 -1
- ppo-LunarLander-v2.zip +2 -2
- ppo-LunarLander-v2/data +18 -18
- ppo-LunarLander-v2/policy.optimizer.pth +2 -2
- ppo-LunarLander-v2/policy.pth +2 -2
- ppo-LunarLander-v2/system_info.txt +2 -2
- replay.mp4 +0 -0
- results.json +1 -1
README.md
CHANGED
@@ -1,11 +1,10 @@
|
|
1 |
---
|
|
|
2 |
tags:
|
3 |
- LunarLander-v2
|
4 |
-
- ppo
|
5 |
- deep-reinforcement-learning
|
6 |
- reinforcement-learning
|
7 |
-
-
|
8 |
-
- deep-rl-course
|
9 |
model-index:
|
10 |
- name: PPO
|
11 |
results:
|
@@ -17,14 +16,22 @@ model-index:
|
|
17 |
type: LunarLander-v2
|
18 |
metrics:
|
19 |
- type: mean_reward
|
20 |
-
value:
|
21 |
name: mean_reward
|
22 |
verified: false
|
23 |
---
|
24 |
|
25 |
-
|
|
|
|
|
26 |
|
27 |
-
|
|
|
28 |
|
29 |
-
|
30 |
-
|
|
|
|
|
|
|
|
|
|
|
|
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:
|
|
|
16 |
type: LunarLander-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
+
value: 247.22 +/- 19.58
|
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
CHANGED
@@ -1 +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 0x7f80e7f21120>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f80e7f211b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f80e7f21240>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f80e7f212d0>", "_build": "<function ActorCriticPolicy._build at 0x7f80e7f21360>", "forward": "<function ActorCriticPolicy.forward at 0x7f80e7f213f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f80e7f21480>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f80e7f21510>", "_predict": "<function ActorCriticPolicy._predict at 0x7f80e7f215a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f80e7f21630>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f80e7f216c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f80e7f21750>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f80e7f12e40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683901015170648284, "learning_rate": 0.0003, "tensorboard_log": null, "_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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVQAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHE05Y5ksjGMAWyUTVUBjAF0lEdAoHMLzRQaaXV9lChoBkdAcsu5IH1OCWgHTRYBaAhHQKBzOUEgW8B1fZQoaAZHQHL+hfWtlqdoB00wAWgIR0CgdE+jmCAddX2UKGgGR0BxiVFOO802aAdNkAFoCEdAoH4SzTnaFnV9lChoBkdAcjKeLNwBHWgHTRgBaAhHQKB+ivRqoIh1fZQoaAZHQHFKGUbDMvBoB00oAWgIR0Cgfri6H0sfdX2UKGgGR0By31enhsInaAdNHQFoCEdAoH7JhfBvaXV9lChoBkdATpa9oN/e+GgHS8JoCEdAoH9GhAWznnV9lChoBkdAcTgzjFQ2uWgHTRMBaAhHQKB/lvWH1vl1fZQoaAZHQGxsLjxTbWVoB00gAWgIR0CggDeXzDoAdX2UKGgGR0Bk53MjeKsNaAdN6ANoCEdAoIBLzmOlwnV9lChoBkdAbspOeJ53T2gHTT4BaAhHQKCAsViWmgt1fZQoaAZHQHF3ayGBWghoB02PAWgIR0CghC0x/NJOdX2UKGgGR0BzWr7hvR7aaAdL8WgIR0CghMNLcsUZdX2UKGgGR0ByX1LvkRzzaAdNngFoCEdAoIUb2Dg62nV9lChoBkdAcm9m29cry2gHTRkBaAhHQKCFN/95yEN1fZQoaAZHQG1FAtFrl/9oB01wAWgIR0CghaVEd/8VdX2UKGgGR0BxReAOJ+DwaAdNkwFoCEdAoIXdBjWkJ3V9lChoBkdAcViVd5Y5k2gHTT8BaAhHQKCGK8an7551fZQoaAZHQHF1q9TP0I1oB01qAWgIR0CghnHKOktVdX2UKGgGR0Bxwk3Ov+wUaAdNbAFoCEdAoIaMBEKE4HV9lChoBkdAbLThsqJ/G2gHTTYBaAhHQKCG/vLowEh1fZQoaAZHQHDiEXxe9jBoB01gAWgIR0CgiMls54nndX2UKGgGR0Bxbf8LronsaAdN1AFoCEdAoImFAE+xGHV9lChoBkdAcL8Nyo4uLGgHTQEBaAhHQKCKpCUornV1fZQoaAZHQG/b60QbuMNoB02fAWgIR0CgiuH3ta6jdX2UKGgGR0BksckWykbhaAdN6ANoCEdAoItqKYRdyHV9lChoBkdAcEC9KEnLJWgHTQACaAhHQKCNTCJGe+V1fZQoaAZHQHL4Zr1uivhoB01DAWgIR0CgjX7hegL7dX2UKGgGR0Bwx95t3wCsaAdNMQFoCEdAoI3nWFvhqHV9lChoBkdAccGNHpbD/GgHTSoBaAhHQKCN8BsANod1fZQoaAZHQHBu6nrIHTtoB01sAWgIR0Cgj1ePikwfdX2UKGgGR0Bx3BH8TBZZaAdNiwFoCEdAoJBIkC3gDXV9lChoBkdAct0czImw7mgHTUIBaAhHQKCQUNZNfw91fZQoaAZHQHFDyNCJGfBoB01mAWgIR0CgkFovSMLndX2UKGgGR0Bx0GrOqvNeaAdNcQFoCEdAoJEIsAeaKHV9lChoBkdAbu+1b7j1f2gHTXABaAhHQKCRIYaYNRZ1fZQoaAZHQHAKgK4QSSNoB00eAWgIR0CgkYj/+85CdX2UKGgGR0BxfoCr92ovaAdNHAFoCEdAoJPguAZsK3V9lChoBkdAbAZ05EMLGGgHTRwBaAhHQKCUOlenhsJ1fZQoaAZHQG+yXj2i+L5oB01kAWgIR0CglM79qDbrdX2UKGgGR0A0MdrO7g89aAdL3mgIR0CglnguAZsLdX2UKGgGR0BuoyUJOWSmaAdNOgFoCEdAoJdN0Lc9GXV9lChoBkdAcxxN+9allGgHTTYBaAhHQKCXWUpNKyx1fZQoaAZHQHA0C0BwMphoB011AWgIR0Cgl29B0ITodX2UKGgGR0ByTlgF5fMOaAdNSAFoCEdAoJgbZlFtsXV9lChoBkdAY/3DOTq0MWgHTegDaAhHQKCYLsZYPoV1fZQoaAZHQHIMeJYT0xxoB00BAWgIR0CgmJE9t/FzdX2UKGgGR0BtQB44ZMtcaAdNHwFoCEdAoJiyIvalDXV9lChoBkdAch03T/hl2GgHTSQBaAhHQKCYzHq/ub91fZQoaAZHQHPWxcVxjrloB00UAWgIR0CgmPvvBrN4dX2UKGgGR0BvbJ7RfF72aAdNPgFoCEdAoKIcPSUkfXV9lChoBkdAcNSjX4CZGGgHTVgBaAhHQKCjTXAdn011fZQoaAZHQHFrnMpw0fpoB00GAWgIR0Cgo6Ye1a4ddX2UKGgGR0BGrijtXxOMaAdL2GgIR0CgpHhuXNTtdX2UKGgGR0BwOb4gzP8iaAdNXwFoCEdAoKU9cnmaIHV9lChoBkdAcKsctXgccWgHTSQBaAhHQKClgX/HYHx1fZQoaAZHQHJ9z1bqyGBoB00LAWgIR0Cgpb1ZTyavdX2UKGgGR0Bxl1cs189faAdNFgFoCEdAoKbAWFev6nV9lChoBkdAbjM8RtgrpmgHTT8BaAhHQKCm4ZUkv9N1fZQoaAZHQHKP4VIqbz9oB00qAWgIR0CgpyPl2eQNdX2UKGgGR0BymAm0E5hjaAdN1AFoCEdAoKfDyMDOknV9lChoBkdAb3kLBKtga2gHTTYBaAhHQKCoGOz6ab51fZQoaAZHQG52tUfgaWJoB00XAWgIR0CgqCGo73fydX2UKGgGR0BwKjhybQTmaAdNOAFoCEdAoKh2pjtojHV9lChoBkdAcYceLvTgEWgHTVMBaAhHQKCojGipNsZ1fZQoaAZHQHIyAXhwVCZoB00DAWgIR0CgqU52ZApsdX2UKGgGR0BuX1WGRFI/aAdNmQFoCEdAoKoz1ZkkKXV9lChoBkdAcRmBT4tYjmgHTQsDaAhHQKCqZX2dupF1fZQoaAZHQHCmPl6qsEJoB00fAWgIR0Cgqv6iCaqkdX2UKGgGR0BxGz1yvLX+aAdNBAFoCEdAoKt4XXRPXXV9lChoBkdAclxPgvUSZmgHTSgBaAhHQKCsLSLIgeR1fZQoaAZHQHGgE47zTWpoB02MAWgIR0CgrG3N9ph4dX2UKGgGR0BysCC17Y03aAdNHwFoCEdAoKyBUYKpk3V9lChoBkdAcgrjD8+A3GgHTQwBaAhHQKCu0slsxfx1fZQoaAZHQHCNtIXj2jBoB00mAWgIR0CgrzQHZ9NOdX2UKGgGR0BwtFCfHxSYaAdNUQFoCEdAoK9WMwUQCnV9lChoBkdAcDxiLVFx42gHTUUBaAhHQKCwuFSsKb91fZQoaAZHQHHW3LeQ+2VoB007AWgIR0CgsOrcTJyRdX2UKGgGR0BxwmBFuvU0aAdNOQFoCEdAoLEBtxdY4nV9lChoBkdAcdrMUAT7EmgHTYsBaAhHQKCxC3MINVl1fZQoaAZHQHK+kKeCkGloB00sAWgIR0CgscxPoFFEdX2UKGgGR0Bxxn18LKFJaAdNDAFoCEdAoLItQuVX3nV9lChoBkdAcOJL7oB7u2gHTSgBaAhHQKCyyXv6TGJ1fZQoaAZHQHEdLVvuPWBoB00sAWgIR0Cgs6m2TgVHdX2UKGgGR0ByfMWZZ0SzaAdN3wFoCEdAoLPPl8w6AHV9lChoBkdAcScKiwjdHmgHTSkBaAhHQKC0CZeAuqZ1fZQoaAZHQHIXHS0BwMpoB00QAWgIR0CgtEUyxiXqdX2UKGgGR0BxS7EaVD8caAdNOgFoCEdAoLVFs54nnnV9lChoBkdAcmIzbN8mbGgHTVQBaAhHQKC1gUkfLcN1fZQoaAZHQHJnHr6ciGFoB00bAWgIR0CgtmnctXgcdX2UKGgGR0By2pUn5SFXaAdNEAFoCEdAoLdJudf9gnV9lChoBkdAbdVoBaLXMGgHTRwBaAhHQKC3p5IH1OF1fZQoaAZHQHCo0e+23KBoB005AWgIR0CguEky1uzhdX2UKGgGR0BwwqpgkTpQaAdNkwFoCEdAoLjTLZBcA3V9lChoBkdAcwSwfhddFGgHTUQBaAhHQKC5OSzPa+N1fZQoaAZHQHJMAcYIjW1oB02YAWgIR0CguUjRc/t6dX2UKGgGR0Br08bxVhkRaAdNGQFoCEdAoLnWgOBlMHV9lChoBkdAcOMiRGMGYGgHTVUBaAhHQKC53LlFMIx1fZQoaAZHQHGxUVi4J/poB00YAWgIR0Cguf9vjwQUdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 310, "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": 16, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
|
|
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 0x7f449f0071c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f449f007250>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f449f0072e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f449f007370>", "_build": "<function ActorCriticPolicy._build at 0x7f449f007400>", "forward": "<function ActorCriticPolicy.forward at 0x7f449f007490>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f449f007520>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f449f0075b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f449f007640>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f449f0076d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f449f007760>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f449f0077f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f449effb600>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1684258161907528944, "learning_rate": 0.0003, "tensorboard_log": null, "_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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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": 16, "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": 128, "n_epochs": 4, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.0+cu118", "GPU Enabled": "False", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
ppo-LunarLander-v2.zip
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ad985930e3db5daea60ed133cd3306533ce4cf15ef61e2ebeec3fcd8b51253e0
|
3 |
+
size 146245
|
ppo-LunarLander-v2/data
CHANGED
@@ -4,20 +4,20 @@
|
|
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
|
8 |
-
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at
|
9 |
-
"reset_noise": "<function ActorCriticPolicy.reset_noise at
|
10 |
-
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at
|
11 |
-
"_build": "<function ActorCriticPolicy._build at
|
12 |
-
"forward": "<function ActorCriticPolicy.forward at
|
13 |
-
"extract_features": "<function ActorCriticPolicy.extract_features at
|
14 |
-
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at
|
15 |
-
"_predict": "<function ActorCriticPolicy._predict at
|
16 |
-
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at
|
17 |
-
"get_distribution": "<function ActorCriticPolicy.get_distribution at
|
18 |
-
"predict_values": "<function ActorCriticPolicy.predict_values at
|
19 |
"__abstractmethods__": "frozenset()",
|
20 |
-
"_abc_impl": "<_abc._abc_data object at
|
21 |
},
|
22 |
"verbose": 1,
|
23 |
"policy_kwargs": {},
|
@@ -26,12 +26,12 @@
|
|
26 |
"_num_timesteps_at_start": 0,
|
27 |
"seed": null,
|
28 |
"action_noise": null,
|
29 |
-
"start_time":
|
30 |
"learning_rate": 0.0003,
|
31 |
"tensorboard_log": null,
|
32 |
"_last_obs": {
|
33 |
":type:": "<class 'numpy.ndarray'>",
|
34 |
-
":serialized:": "
|
35 |
},
|
36 |
"_last_episode_starts": {
|
37 |
":type:": "<class 'numpy.ndarray'>",
|
@@ -45,13 +45,13 @@
|
|
45 |
"_stats_window_size": 100,
|
46 |
"ep_info_buffer": {
|
47 |
":type:": "<class 'collections.deque'>",
|
48 |
-
":serialized:": "
|
49 |
},
|
50 |
"ep_success_buffer": {
|
51 |
":type:": "<class 'collections.deque'>",
|
52 |
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
53 |
},
|
54 |
-
"_n_updates":
|
55 |
"observation_space": {
|
56 |
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
57 |
":serialized:": "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",
|
@@ -83,7 +83,7 @@
|
|
83 |
"ent_coef": 0.01,
|
84 |
"vf_coef": 0.5,
|
85 |
"max_grad_norm": 0.5,
|
86 |
-
"batch_size":
|
87 |
"n_epochs": 4,
|
88 |
"clip_range": {
|
89 |
":type:": "<class 'function'>",
|
|
|
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 0x7f449f0071c0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f449f007250>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f449f0072e0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f449f007370>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f449f007400>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f449f007490>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f449f007520>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f449f0075b0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f449f007640>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f449f0076d0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f449f007760>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f449f0077f0>",
|
19 |
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f449effb600>"
|
21 |
},
|
22 |
"verbose": 1,
|
23 |
"policy_kwargs": {},
|
|
|
26 |
"_num_timesteps_at_start": 0,
|
27 |
"seed": null,
|
28 |
"action_noise": null,
|
29 |
+
"start_time": 1684258161907528944,
|
30 |
"learning_rate": 0.0003,
|
31 |
"tensorboard_log": null,
|
32 |
"_last_obs": {
|
33 |
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAGZm6ziRo9E9oaW1u5mcSr5QVZ+8Jqr9OgAAAAAAAAAAM0VlPIW73bnkkow5hv8WNOwGF7smgaO4AACAPwAAgD+zPsa+QxZBP401AL4Ueru+tbiwvqHTFT4AAAAAAAAAADPNsr1S2Mu5K+HoOgBpvjWU3yU7IJYHugAAgD8AAIA/rWt9PlTqMb1QGxE8YRi1ulBTnL6LZ4K7AACAPwAAgD/gDls+ayxaP8oo/z0HILO+i3UnPlLCpb0AAAAAAAAAAJq2tTwUYKq6CGe1NuTsrjEDIYY6KO7TtQAAgD8AAIA/ZuaZvFLw3bkyvou7wrhDOKxPbDo9wHw4AACAPwAAgD/mpUY95raiPyyQlT7NfrS+rQ+2PMbyfT0AAAAAAAAAALNkZT20TUM+rdUvPbxqM74IXYA9Js/8vAAAAAAAAAAAM+muvXZLnj8NrEK+lPKjvqJPBr5AMuO3AAAAAAAAAACaBiy99sxgukaEgTiBe+cz/c9/N2LMlbcAAIA/AACAP5pl17z23B26XFsmOL5BGzP2kW26KjtGtwAAgD8AAIA/M1N3PcuOYj87bkG8sUCIvts/CTzyh+68AAAAAAAAAABA/oW9e+KluhJZPzrctxY2GOaBuqriDDUAAAAAAACAPzNfKz0pVDu6PVYtuQNg3DUgH547zHFEtQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
|
35 |
},
|
36 |
"_last_episode_starts": {
|
37 |
":type:": "<class 'numpy.ndarray'>",
|
|
|
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": 248,
|
55 |
"observation_space": {
|
56 |
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
57 |
":serialized:": "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",
|
|
|
83 |
"ent_coef": 0.01,
|
84 |
"vf_coef": 0.5,
|
85 |
"max_grad_norm": 0.5,
|
86 |
+
"batch_size": 128,
|
87 |
"n_epochs": 4,
|
88 |
"clip_range": {
|
89 |
":type:": "<class 'function'>",
|
ppo-LunarLander-v2/policy.optimizer.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fd24b202bcedaf0f7ce4d19f8d676dbaa7e616b68243230e902952782788f2f2
|
3 |
+
size 87545
|
ppo-LunarLander-v2/policy.pth
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d4b1edec048ee07eaf84d4ec3d4d9f3a8470e0f8aa5482253c711ad978fa26bc
|
3 |
+
size 43201
|
ppo-LunarLander-v2/system_info.txt
CHANGED
@@ -1,8 +1,8 @@
|
|
1 |
-
- OS: Linux-5.
|
2 |
- Python: 3.10.11
|
3 |
- Stable-Baselines3: 2.0.0a5
|
4 |
- PyTorch: 2.0.0+cu118
|
5 |
-
- GPU Enabled:
|
6 |
- Numpy: 1.22.4
|
7 |
- Cloudpickle: 2.2.1
|
8 |
- Gymnasium: 0.28.1
|
|
|
1 |
+
- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
|
2 |
- Python: 3.10.11
|
3 |
- Stable-Baselines3: 2.0.0a5
|
4 |
- PyTorch: 2.0.0+cu118
|
5 |
+
- GPU Enabled: False
|
6 |
- Numpy: 1.22.4
|
7 |
- Cloudpickle: 2.2.1
|
8 |
- Gymnasium: 0.28.1
|
replay.mp4
CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
|
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"
|
|
|
1 |
+
{"mean_reward": 247.21856178353596, "std_reward": 19.584040831544083, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-16T17:59:11.046524"}
|