Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning

This paper investigates how action information can be incorporated into the state update function of a recurrent cell within recurrent neural networks (RNNs) for reinforcement learning (RL). The authors discuss several choices and empirically evaluate the resulting architectures on illustrative domains.