RL Playground

A collection of RL algorithms.

Existing models

See model_zoo for pretrained models on various environments.

Running a model

For running a general model:

python -m rl.run --env=<env>

where <env> is the name of a configuration file under configs/ (like snake_spr).

For running a model using explicit multithreading:

mpiexec -n <n_threads> python -m rl.run_mpi

which splits up rollouts and gradient computation onto <n_threads> threads. Currently, this only makes sense for PPO as the training bottleneck for DQN-like agents are the update steps (which TensorFlow parallelizes across threads) and not environment interaction.

For running a model on GPU:

CUDA_VISIBLE_DEVICES=<device> python -m rl.run --env=<env>

where <device> is the id of the GPU to use (i.e. CUDA_VISIBLE_DEVICES=0).

Setup

1. Clone the repo
2. In the root of the repo, run scripts/setup.sh
3. Ensure you have pre-commit installed. Run pre-commit install in the root of the repo