  A major challenge in reinforcement learning is the design of exploration
strategies, especially for environments with sparse reward structures and
continuous state and action spaces. Intuitively, if the reinforcement signal is
very scarce, the agent should rely on some form of short-term memory in order
to cover its environment efficiently. We propose a new exploration method,
based on two intuitions: (1) the choice of the next exploratory action should
depend not only on the (Markovian) state of the environment, but also on the
agent's trajectory so far, and (2) the agent should utilize a measure of spread
in the state space to avoid getting stuck in a small region. Our method
leverages concepts often used in statistical physics to provide explanations
for the behavior of simplified (polymer) chains in order to generate persistent
(locally self-avoiding) trajectories in state space. We discuss the theoretical
properties of locally self-avoiding walks and their ability to provide a kind
of short-term memory through a decaying temporal correlation within the
trajectory. We provide empirical evaluations of our approach in a simulated 2D
navigation task, as well as higher-dimensional MuJoCo continuous control
locomotion tasks with sparse rewards.
