Reinforcement learning algorithms in RLlib and PyTorch.
Project description
Reinforcement learning algorithms in RLlib and PyTorch.
Introduction
Raylab provides agents and environments to be used with a normal RLlib/Tune setup.
import ray
from ray import tune
import raylab
def main():
raylab.register_all_agents()
raylab.register_all_environments()
ray.init()
tune.run(
"NAF",
local_dir=...,
stop={"timesteps_total": 100000},
config={
"env": "CartPoleSwingUp-v0",
"exploration_config": {
"type": tune.grid_search([
"raylab.utils.exploration.GaussianNoise",
"raylab.utils.exploration.ParameterNoise"
])
}
...
},
)
if __name__ == "__main__":
main()
One can then visualize the results using raylab dashboard
Installation
pip install raylab
Algorithms
Paper |
Agent Name |
ACKTR |
|
TRPO |
|
NAF |
|
SVG(inf)/SVG(1)/SoftSVG |
|
SoftAC |
|
Streamlined Off-Policy (DDPG) |
SOP |
MBPO |
|
MAGE |
Command-line interface
For a high-level description of the available utilities, run raylab --help
Usage: raylab [OPTIONS] COMMAND [ARGS]...
RayLab: Reinforcement learning algorithms in RLlib.
Options:
--help Show this message and exit.
Commands:
dashboard Launch the experiment dashboard to monitor training progress.
episodes Launch the episode dashboard to monitor state and action...
experiment Launch a Tune experiment from a config file.
find-best Find the best experiment checkpoint as measured by a metric.
info View information about an agent's config parameters.
rollout Wrap `rllib rollout` with customized options.
test-module Launch dashboard to test generative models from a checkpoint.
Packages
The project is structured as follows
raylab |-- agents # Trainer and Policy classes |-- cli # Command line utilities |-- envs # Gym environment registry and utilities |-- logger # Tune loggers |-- policy # Extensions and customizations of RLlib's policy API | |-- losses # RL loss functions | |-- modules # PyTorch neural network modules for TorchPolicy |-- pytorch # PyTorch extensions |-- utils # miscellaneous utilities
Project details
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