  Trust-region methods have yielded state-of-the-art results in policy search.
A common approach is to use KL-divergence to bound the region of trust
resulting in a natural gradient policy update. We show that the natural
gradient and trust region optimization are equivalent if we use the natural
parameterization of a standard exponential policy distribution in combination
with compatible value function approximation. Moreover, we show that standard
natural gradient updates may reduce the entropy of the policy according to a
wrong schedule leading to premature convergence. To control entropy reduction
we introduce a new policy search method called compatible policy search (COPOS)
which bounds entropy loss. The experimental results show that COPOS yields
state-of-the-art results in challenging continuous control tasks and in
discrete partially observable tasks.
