Exponential moving average based multiagent reinforcement learning algorithms

Two multi-agent policy iteration learning algorithms are proposed in this work. The two proposed algorithms use the exponential moving average approach along with the Q-learning algorithm as a basis to update the policy for the learning agent so that the agent's policy converges to a Nash equilibrium policy. The first proposed algorithm uses a constant learning rate when updating the policy of the learning agent, while the second proposed algorithm uses two different decaying learning rates. These two decaying learning rates are updated based on either the Win-or-Learn-Fast (WoLF) mechanism or the Win-or-Learn-Slow (WoLS) mechanism. The WoLS mechanism is introduced in this article to make the algorithm learn fast when it is winning and learn slowly when it is losing. The second proposed algorithm uses the rewards received by the learning agent to decide which mechanism (WoLF mechanism or WoLS mechanism) to use for the game being learned. The proposed algorithms have been theoretically analyzed and a mathematical proof of convergence to pure Nash equilibrium is provided for each algorithm. In the case of games with mixed Nash equilibrium, our mathematical analysis shows that the second proposed algorithm converges to an equilibrium. Although our mathematical analysis does not explicitly show that the second proposed algorithm converges to a Nash equilibrium, our simulation results indicate that the second proposed algorithm does converge to Nash equilibrium. The proposed algorithms are examined on a variety of matrix and stochastic games. Simulation results show that the second proposed algorithm converges in a wider variety of situations than state-of-the-art multi-agent reinforcement learning algorithms.