  Entropy regularization is used to get improved optimization performance in
reinforcement learning tasks. A common form of regularization is to maximize
policy entropy to avoid premature convergence and lead to more stochastic
policies for exploration through action space. However, this does not ensure
exploration in the state space. In this work, we instead consider the
distribution of discounted weighting of states, and propose to maximize the
entropy of a lower bound approximation to the weighting of a state, based on
latent space state representation. We propose entropy regularization based on
the marginal state distribution, to encourage the policy to have a more uniform
distribution over the state space for exploration. Our approach based on
marginal state distribution achieves superior state space coverage on complex
gridworld domains, that translate into empirical gains in sparse reward 3D maze
navigation and continuous control domains compared to entropy regularization
with stochastic policies.
