Scalable Real-Time Recurrent Learning Using Sparse Connections and Selective Learning

State construction from sensory observations is an important component of a reinforcement learning agent. One solution for state construction is to use recurrent neural networks. Back-propagation through time (BPTT), and real-time recurrent learning (RTRL) are two popular gradient-based methods for recurrent learning. BPTT requires the complete sequence of observations before computing gradients and is unsuitable for online real-time updates. RTRL can do online updates but scales poorly to large networks. In this paper, we propose two constraints that make RTRL scalable. We show that by either decomposing the network into independent modules, or learning the network incrementally, we can make RTRL scale linearly with the number of parameters. Unlike prior scalable gradient estimation algorithms, such as UORO and Truncated-BPTT, our algorithms do not add noise or bias to the gradient estimate. Instead, they trade-off the functional capacity of the network to achieve scalable learning. We demonstrate the effectiveness of our approach over Truncated-BPTT on a benchmark inspired by animal learning and by doing policy evaluation for pre-trained Rainbow-DQN agents in the Arcade Learning Environment (ALE).