Actor-critic is implicitly biased towards high entropy optimal policies
This addresses the problem of simplifying reinforcement learning algorithms by removing explicit exploration and mixing assumptions, which is incremental but impactful for theoretical understanding.
The paper shows that a basic actor-critic method, without explicit regularization or exploration, inherently prefers high-entropy optimal policies in linear MDPs, enabling convergence without uniform mixing assumptions. This result is demonstrated through analysis on a single trajectory with no resets, dropping prior requirements for mixing conditions.
We show that the simplest actor-critic method -- a linear softmax policy updated with TD through interaction with a linear MDP, but featuring no explicit regularization or exploration -- does not merely find an optimal policy, but moreover prefers high entropy optimal policies. To demonstrate the strength of this bias, the algorithm not only has no regularization, no projections, and no exploration like $ε$-greedy, but is moreover trained on a single trajectory with no resets. The key consequence of the high entropy bias is that uniform mixing assumptions on the MDP, which exist in some form in all prior work, can be dropped: the implicit regularization of the high entropy bias is enough to ensure that all chains mix and an optimal policy is reached with high probability. As auxiliary contributions, this work decouples concerns between the actor and critic by writing the actor update as an explicit mirror descent, provides tools to uniformly bound mixing times within KL balls of policy space, and provides a projection-free TD analysis with its own implicit bias which can be run from an unmixed starting distribution.