A REINFORCEMENT LEARNING ENVIRONMENT FOR MATHEMATICAL REASONING VIA PROGRAM SYNTHESIS

Alok Singh, Joseph W. Palermo, Johnny Ye

Abstract:

We convert the DeepMind Mathematics Dataset into a reinforcement learning environment by interpreting it as a program synthesis problem. Each action taken
in the environment adds an operator or an input into a discrete compute graph.
Graphs which compute correct answers yield positive reward, enabling the optimization of a policy to construct compute graphs conditioned on problem statements. Baseline models are trained using Double DQN on various subsets of
problem types, demonstrating the capability to learn to correctly construct graphs
despite the challenges of combinatorial explosion and noisy rewards.