This repository contains a PyTorch implementation of the anti-concentrated confidence bonus for promoting exploration in deep reinforcement learning. For more information, check out our ICLR 2022 paper, Anti-Concentrated Confidence Bonuses for Scalable Exploration. Our code was built off of jcwleo's impelementation of Random Network Distillation.
Required dependencies can be found in setup.py.
python run.py --intrinsic acb --env breakout
runs an experiment using ACB in the Atari game Breakout.
python run.py --intrinsic rnd --env seaquest --extrinsic
runs an experiment using RND in the atari game Seaquest. The extrinsic flag allows the agent to be trained jointly on intrinsic and extrinsic rewards; by default only the specified intrinsic rewards are used.