Off-policy Temporal Difference (TD) learning methods, when combined with function approximators, suffer from the risk of divergence, a phenomenon known as the deadly triad. It has long been noted that some feature representations work better than others. In this paper we investigate how feature normalization can prevent divergence and improve training. Our method, which we call CrossNorm, can be regarded as a new variant of batch normalization that re-centers data for multi-modal distributions, which occur in the off-policy TD updates. We show empirically that CrossNorm improves the stability of the learning process. We apply CrossNorm to DDPG and TD3 and achieve stable training and improved performance across a range of MuJoCo benchmark tasks. Moreover, for the first time, we are able to train DDPG stably without the use of target networks.

In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector. Top participants were invited to describe their algorithms. In this work, we describe the challenge and present thirteen solutions that used deep reinforcement learning approaches. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each team implemented different modifications of the known algorithms by, for example, dividing the task into subtasks, learning low-level control, or by incorporating expert knowledge and using imitation learning.