424 citations · 1.3k across the 29 of their papers we have counts for
4 papers · 2 filters
Understanding Self-Predictive Learning for Reinforcement Learning
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond +13
We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their…
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning
Yunhao Tang, Mark Rowland, Rémi Munos +3
We study the multi-step off-policy learning approach to distributional RL. Despite the apparent similarity between value-based RL and distributional RL, our study reveals intriguin…
Learning Dynamics and Generalization in Reinforcement Learning
Clare Lyle, Mark Rowland, Will Dabney +2
Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a potentially discontinuous value function, and generalizing well to new observations. In this…
Understanding and Preventing Capacity Loss in Reinforcement Learning
Clare Lyle, Mark Rowland, Will Dabney
The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a notoriously difficult problem domain for the application of neural networks. We identi…