Paper Unraveled

Paper Unraveled: Revisiting Fundamentals of Experience Replay (Fedus et al., 2020)

Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding. We therefore present a systematic...

Paper Unraveled: Exploration by Random Network Distillation (Burda et al., 2018)

We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus...

Paper Unraveled: A Deeper Look at Experience Replay (Zhang and Sutton, 2017)

Recently experience replay is widely used in various deep reinforcement learning (RL) algorithms, in this paper we rethink the utility of experience replay. It introduces...

Paper Unraveled: Neural Fitted Q Iteration (Riedmiller, 2005)

This paper introduces NFQ, an algorithm for efficient and effective training of a Q-value function represented by a multi-layer perceptron. Based on the principle of...