Training Equilibria in Reinforcement Learning

In partially observable environments, reinforcement learning algorithms such as policy gradient and Q-learning may have multiple equilibria---policies that are stable under further training---and can converge to policies that are strictly suboptimal. Prior work blames insufficient exploration, but suboptimal equilibria can arise despite full exploration and other favorable circumstances like a flexible policy parametrization. We show theoretically that the core problem is that in partially observed environments, an agent's past actions induce a distribution on hidden states. Equipping the policy with memory helps it model the hidden state and leads to convergence to a higher reward equilibrium, \emph{even when there exists a memoryless optimal policy}. Experiments show that policies with insufficient memory tend to learn to use the environment as auxiliary memory,and parameter noise helps policies escape suboptimal equilibria.