Combining No-regret and Q-learning
This work addresses a limitation in reinforcement learning for settings like Markov games, offering a novel approach for scenarios without terminal states or perfect recall, though it is incremental as it builds on CFR and Q-learning.
The paper tackled the problem of relaxing the requirements of terminal states and perfect recall in Counterfactual Regret Minimization (CFR) by introducing a local no-regret learning (LONR) algorithm, which achieved last iterate convergence in challenging NoSDE games where no prior algorithm was known to converge.
Counterfactual Regret Minimization (CFR) has found success in settings like poker which have both terminal states and perfect recall. We seek to understand how to relax these requirements. As a first step, we introduce a simple algorithm, local no-regret learning (LONR), which uses a Q-learning-like update rule to allow learning without terminal states or perfect recall. We prove its convergence for the basic case of MDPs (and limited extensions of them) and present empirical results showing that it achieves last iterate convergence in a number of settings, most notably NoSDE games, a class of Markov games specifically designed to be challenging to learn where no prior algorithm is known to achieve convergence to a stationary equilibrium even on average.