Sanjay-Papaiahgari
commited on
Commit
•
aade21e
1
Parent(s):
ae0fe0e
Lunar Lander agent trained using PPO with MlpPolicy for 1e6 steps
Browse files- README.md +37 -0
- config.json +1 -0
- lunar-lander-v2.zip +3 -0
- lunar-lander-v2/_stable_baselines3_version +1 -0
- lunar-lander-v2/data +94 -0
- lunar-lander-v2/policy.optimizer.pth +3 -0
- lunar-lander-v2/policy.pth +3 -0
- lunar-lander-v2/pytorch_variables.pth +3 -0
- lunar-lander-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: MlpPolicy
|
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: 281.80 +/- 19.89
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **MlpPolicy** Agent playing **LunarLander-v2**
|
25 |
+
This is a trained model of a **MlpPolicy** 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 0x7fe3167265e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe316726670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe316726700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe316726790>", "_build": "<function ActorCriticPolicy._build at 0x7fe316726820>", "forward": "<function ActorCriticPolicy.forward at 0x7fe3167268b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe316726940>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe3167269d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe316726a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe316726af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe316726b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe31671ce40>"}, "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": 1670654017256989612, "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:": "gAWVJxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI4PJYM7Ibb0CUhpRSlIwBbJRLmIwBdJRHQKBThP8AJcB1fZQoaAZoCWgPQwgW9x+ZDudvQJSGlFKUaBVLnmgWR0CgU40btJFtdX2UKGgGaAloD0MIUtMuphmocUCUhpRSlGgVS8doFkdAoFOWpQ1rI3V9lChoBmgJaA9DCHbFjPD2rXFAlIaUUpRoFUuwaBZHQKBT1ymygPF1fZQoaAZoCWgPQwhjnL8JhcNwQJSGlFKUaBVLy2gWR0CgU+ornTy8dX2UKGgGaAloD0MIixagbbU0b0CUhpRSlGgVS7toFkdAoFQC6MBIWnV9lChoBmgJaA9DCBsuck8X+3FAlIaUUpRoFUuqaBZHQKBUFRTCLuR1fZQoaAZoCWgPQwgQJVryuGdyQJSGlFKUaBVLtmgWR0CgVELaVUuMdX2UKGgGaAloD0MIyuL+I9M6YECUhpRSlGgVTegDaBZHQKBUmR/ViF11fZQoaAZoCWgPQwiVSKKXUcRxQJSGlFKUaBVLxWgWR0CgVLn2h7E6dX2UKGgGaAloD0MIWp9yTFZScUCUhpRSlGgVS5poFkdAoFTL5j6N2nV9lChoBmgJaA9DCLVSCOSSqHJAlIaUUpRoFUveaBZHQKBVRlNlAeJ1fZQoaAZoCWgPQwgPJsXHJ01yQJSGlFKUaBVL2mgWR0CgVVAjyFwldX2UKGgGaAloD0MItTUiGIcub0CUhpRSlGgVS55oFkdAoFV8F6iTMnV9lChoBmgJaA9DCGgDsAGRCHBAlIaUUpRoFUugaBZHQKBVklhPTG51fZQoaAZoCWgPQwhlUdhF0fFwQJSGlFKUaBVLxWgWR0CgVZWcJ+lTdX2UKGgGaAloD0MIHw99dys3c0CUhpRSlGgVS/5oFkdAoFYw8dPtUnV9lChoBmgJaA9DCHu/0Y7bWXFAlIaUUpRoFUu8aBZHQKBWPkCmuT11fZQoaAZoCWgPQwikwthCkFJwQJSGlFKUaBVLnGgWR0CgVoRCx/utdX2UKGgGaAloD0MIQ5CDEqZFcUCUhpRSlGgVTQMBaBZHQKBWyn/DLr51fZQoaAZoCWgPQwh1dFyN7Io9QJSGlFKUaBVLbGgWR0CgVtsunMt9dX2UKGgGaAloD0MIigJ9Ik9vcUCUhpRSlGgVS89oFkdAoFbY8jiXIHV9lChoBmgJaA9DCK2kFd8QOXFAlIaUUpRoFUvtaBZHQKBW/yQPqcF1fZQoaAZoCWgPQwg5RUdyuQt0QJSGlFKUaBVLv2gWR0CgVx5mAbyZdX2UKGgGaAloD0MIAFMGDug5ckCUhpRSlGgVTQIBaBZHQKBXTgFX7tR1fZQoaAZoCWgPQwjXEvJBD4JyQJSGlFKUaBVLp2gWR0CgV2FNtZV5dX2UKGgGaAloD0MIoMA7+fQcRUCUhpRSlGgVS11oFkdAoFdtolD4QHV9lChoBmgJaA9DCBNJ9DKKX3JAlIaUUpRoFUuTaBZHQKBXbM0xdpt1fZQoaAZoCWgPQwh3LSEfdCNxQJSGlFKUaBVL3GgWR0CgV4coH9m6dX2UKGgGaAloD0MIs2Dij6LqckCUhpRSlGgVTUIBaBZHQKBX0prk8zR1fZQoaAZoCWgPQwjv/nivGthyQJSGlFKUaBVLw2gWR0CgV/I3zcyndX2UKGgGaAloD0MI5bZ9j3onckCUhpRSlGgVS/loFkdAoFhYdGRV63V9lChoBmgJaA9DCBE5fT1fU3NAlIaUUpRoFUvFaBZHQKBYliwSrYJ1fZQoaAZoCWgPQwh23sZmh4RxQJSGlFKUaBVLyGgWR0CgWOyE+PildX2UKGgGaAloD0MIfc9IhMYpcUCUhpRSlGgVS8BoFkdAoFkgM8YAKnV9lChoBmgJaA9DCO9yEd/JU3JAlIaUUpRoFUvPaBZHQKBZPqxC6Yp1fZQoaAZoCWgPQwgUXoJTn/VuQJSGlFKUaBVLs2gWR0CgWWl23azvdX2UKGgGaAloD0MIBRbAlIHpcECUhpRSlGgVS8VoFkdAoFlwixFAmnV9lChoBmgJaA9DCFJ/vcKClHBAlIaUUpRoFUvPaBZHQKBZcBClabF1fZQoaAZoCWgPQwgyHxDozGZzQJSGlFKUaBVL0GgWR0CgWfpqASWadX2UKGgGaAloD0MIVg3C3G75ckCUhpRSlGgVS7NoFkdAoFoW+fywwHV9lChoBmgJaA9DCNlAuti0nnJAlIaUUpRoFUvAaBZHQKBaG0UoKD11fZQoaAZoCWgPQwhj8DDtG3txQJSGlFKUaBVLm2gWR0CgWjcghbGFdX2UKGgGaAloD0MIPX5v05+scECUhpRSlGgVS49oFkdAoFpQjY7JXHV9lChoBmgJaA9DCJCEfTsJRHFAlIaUUpRoFUuNaBZHQKBapm9xp+N1fZQoaAZoCWgPQwhDAdvBCGZvQJSGlFKUaBVLn2gWR0CgW1AvlEJCdX2UKGgGaAloD0MIl3K+2DslcECUhpRSlGgVS7toFkdAoFuU5n13+3V9lChoBmgJaA9DCLPO+L64Lm9AlIaUUpRoFUuuaBZHQKBbyrI5o5B1fZQoaAZoCWgPQwgFqKllqy9yQJSGlFKUaBVLqGgWR0CgXPAb6xgRdX2UKGgGaAloD0MIqBq9GuDpckCUhpRSlGgVS/JoFkdAoFz4DTz/ZXV9lChoBmgJaA9DCMK/CBrzW3NAlIaUUpRoFUvKaBZHQKBdPItlI3B1fZQoaAZoCWgPQwj7A+W2PctyQJSGlFKUaBVNCwFoFkdAoF1YqXnhbXV9lChoBmgJaA9DCH7Er1iDTXJAlIaUUpRoFUvcaBZHQKBdX3oLXtl1fZQoaAZoCWgPQwg+B5YjZO1xQJSGlFKUaBVL32gWR0CgXZF+Vkc0dX2UKGgGaAloD0MIzGJi83GlXkCUhpRSlGgVTegDaBZHQKBdzPDYRNB1fZQoaAZoCWgPQwiWP98WbE5xQJSGlFKUaBVL72gWR0CgXfkQXhwVdX2UKGgGaAloD0MIr84xILvVckCUhpRSlGgVS+VoFkdAoF5L4xk/bHV9lChoBmgJaA9DCIm1+BSA/G9AlIaUUpRoFUugaBZHQKBeacaOxSp1fZQoaAZoCWgPQwhWfa624oVwQJSGlFKUaBVLuWgWR0CgXpRwQ176dX2UKGgGaAloD0MIZyyazk4xckCUhpRSlGgVS9FoFkdAoF6r1bqyGHV9lChoBmgJaA9DCPoK0owFDHFAlIaUUpRoFUubaBZHQKBfndcjZ+R1fZQoaAZoCWgPQwhP6svSjq9xQJSGlFKUaBVLomgWR0CgX6BLoOhCdX2UKGgGaAloD0MIOsyXF+D2cUCUhpRSlGgVS7NoFkdAoF+jFqBVdXV9lChoBmgJaA9DCP63kh1bpXBAlIaUUpRoFUucaBZHQKBgBTMqz7d1fZQoaAZoCWgPQwicxCCwMgZyQJSGlFKUaBVL5GgWR0CgYFC1JDmbdX2UKGgGaAloD0MI4+Ko3IQTc0CUhpRSlGgVS9RoFkdAoGCkYGdI5HV9lChoBmgJaA9DCJYJv9RPnW5AlIaUUpRoFUupaBZHQKBgufvnbIt1fZQoaAZoCWgPQwh5lEp4gtVyQJSGlFKUaBVLtmgWR0CgYQhQvYe1dX2UKGgGaAloD0MINX9Ma9NAckCUhpRSlGgVS9hoFkdAoGEZHqeK9HV9lChoBmgJaA9DCFw+kpKeYHBAlIaUUpRoFUu0aBZHQKBhLitq59V1fZQoaAZoCWgPQwg6yyxCMTlwQJSGlFKUaBVLr2gWR0CgYTNeMQ2/dX2UKGgGaAloD0MIuAGfH0ZuckCUhpRSlGgVS51oFkdAoGHouVX3g3V9lChoBmgJaA9DCLZJRWNtDHFAlIaUUpRoFUusaBZHQKBiJCKrJbN1fZQoaAZoCWgPQwi139qJkq5zQJSGlFKUaBVLvGgWR0CgYmbQLNOedX2UKGgGaAloD0MIITzaOCIfckCUhpRSlGgVS6poFkdAoGKM8vEjxHV9lChoBmgJaA9DCLfQlQhUrXFAlIaUUpRoFUuYaBZHQKBi7lvqC6J1fZQoaAZoCWgPQwgXuDzWzIhyQJSGlFKUaBVLvWgWR0CgYyxYJVsDdX2UKGgGaAloD0MIbHnlelvBcECUhpRSlGgVS6toFkdAoGNPP1L8JnV9lChoBmgJaA9DCEc7bvjdeHBAlIaUUpRoFUujaBZHQKBjqLJjlPt1fZQoaAZoCWgPQwiOjxZnzBRyQJSGlFKUaBVLsWgWR0CgY7uvECNkdX2UKGgGaAloD0MISmBzDp5VbkCUhpRSlGgVS6hoFkdAoGPF5UtI1HV9lChoBmgJaA9DCMS0b+5vTnJAlIaUUpRoFUuoaBZHQKBklXPqs2h1fZQoaAZoCWgPQwiSIcfWM6dwQJSGlFKUaBVL4WgWR0CgZJhhpg1FdX2UKGgGaAloD0MI9tIUAc4Ab0CUhpRSlGgVS6poFkdAoGUp15jYqXV9lChoBmgJaA9DCCkmb4AZt2NAlIaUUpRoFU3oA2gWR0CgZTLFGXoldX2UKGgGaAloD0MIba0vEtpccECUhpRSlGgVS7RoFkdAoGVz9jwx33V9lChoBmgJaA9DCJ0v9l68nHFAlIaUUpRoFUuGaBZHQKBlfl0YCQt1fZQoaAZoCWgPQwhL6Zle4s9kQJSGlFKUaBVN6ANoFkdAoGXeHi3ocXV9lChoBmgJaA9DCJFkVu/wQWNAlIaUUpRoFU3oA2gWR0CgZe18CxNZdX2UKGgGaAloD0MI+N10yw77Y0CUhpRSlGgVTegDaBZHQKBl7YKYzBR1fZQoaAZoCWgPQwiR7Xw/tUZyQJSGlFKUaBVLwWgWR0CgZf/+CK77dX2UKGgGaAloD0MItf0rKw1KcUCUhpRSlGgVS7VoFkdAoGYLApKBd3V9lChoBmgJaA9DCBTQRNjwtHJAlIaUUpRoFUu6aBZHQKBmenDziCJ1fZQoaAZoCWgPQwhYN94dGaxvQJSGlFKUaBVLp2gWR0CgZuaBRQ7+dX2UKGgGaAloD0MIRiOfVzzIcECUhpRSlGgVS+BoFkdAoGb9zOoo/nV9lChoBmgJaA9DCG07bY1IZHJAlIaUUpRoFUu7aBZHQKBnLBeokzJ1fZQoaAZoCWgPQwivljszwZ1vQJSGlFKUaBVLs2gWR0CgZ4a0QbuMdX2UKGgGaAloD0MIelImNXT7cUCUhpRSlGgVS8FoFkdAoGe69mHxjXV9lChoBmgJaA9DCPoI/OHn+XFAlIaUUpRoFUvIaBZHQKBoDtw71Zl1fZQoaAZoCWgPQwg8g4b+SUBxQJSGlFKUaBVLyWgWR0CgaBsP8Q7LdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 620, "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": 20, "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"}}
|
lunar-lander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:22132966184454c81d0a7aa58abb09a17d2fc1fa084ad8f5dfc17f2df63c3beb
|
3 |
+
size 147097
|
lunar-lander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.6.2
|
lunar-lander-v2/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 0x7fe3167265e0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe316726670>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe316726700>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe316726790>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fe316726820>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fe3167268b0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe316726940>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fe3167269d0>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe316726a60>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe316726af0>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe316726b80>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7fe31671ce40>"
|
20 |
+
},
|
21 |
+
"verbose": 1,
|
22 |
+
"policy_kwargs": {},
|
23 |
+
"observation_space": {
|
24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
25 |
+
":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
|
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": 1670654017256989612,
|
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": 620,
|
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": 20,
|
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 |
+
}
|
lunar-lander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:09a5656336e1154dd3f5a5e8c39ad929bfae6e0601a13915e413f3ba6785e2a5
|
3 |
+
size 87929
|
lunar-lander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:98317516b4082bb0076e83e69b7daee85b0054b2ca44407f0cc7441d4d5d6551
|
3 |
+
size 43201
|
lunar-lander-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
|
lunar-lander-v2/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 (195 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 281.8016586924725, "std_reward": 19.890456554695206, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-10T07:14:07.897165"}
|