  Maximum Entropy (MaxEnt) reinforcement learning is a powerful learning
paradigm which seeks to maximize return under entropy regularization. However,
action entropy does not necessarily coincide with state entropy, e.g., when
multiple actions produce the same transition. Instead, we propose to maximize
the transition entropy, i.e., the entropy of next states. We show that
transition entropy can be described by two terms; namely, model-dependent
transition entropy and action redundancy. Particularly, we explore the latter
in both deterministic and stochastic settings and develop tractable
approximation methods in a near model-free setup. We construct algorithms to
minimize action redundancy and demonstrate their effectiveness on a synthetic
environment with multiple redundant actions as well as contemporary benchmarks
in Atari and Mujoco. Our results suggest that action redundancy is a
fundamental problem in reinforcement learning.
