  Deep reinforcement learning algorithms often use two networks for value
function optimization: an online network, and a target network that tracks the
online network with some delay. Using two separate networks enables the agent
to hedge against issues that arise when performing bootstrapping. In this paper
we endow two popular deep reinforcement learning algorithms, namely DQN and
Rainbow, with updates that incentivize the online network to remain in the
proximity of the target network. This improves the robustness of deep
reinforcement learning in presence of noisy updates. The resultant agents,
called DQN Pro and Rainbow Pro, exhibit significant performance improvements
over their original counterparts on the Atari benchmark demonstrating the
effectiveness of this simple idea in deep reinforcement learning. The code for
our paper is available here:
Github.com/amazon-research/fast-rl-with-slow-updates.
