This paper investigates the use of multi-agent reinforcement learning (MARL) to address distributed channel access in wireless local area networks. In particular, we consider the challenging yet more practical case where the agents heterogeneously adopt value-based or policy-based reinforcement learning algorithms to train the model. We propose a heterogeneous MARL training framework, named QPMIX, which adopts a centralized training with distributed execution paradigm to enable heterogeneous agents to collaborate. Moreover, we theoretically prove the convergence of the proposed heterogeneous MARL method when using the linear value function approximation. Our method maximizes the network throughput and ensures fairness among stations, therefore, enhancing the overall network performance. Simulation results demonstrate that the proposed QPMIX algorithm improves throughput, mean delay, delay jitter, and collision rates compared with conventional carrier-sense multiple access with collision avoidance in the saturated traffic scenario. Furthermore, the QPMIX is shown to be robust in unsaturated and delay-sensitive traffic scenarios, and promotes cooperation among heterogeneous agents.

After a weeklong break, I am back again with part 2 of my Reinforcement Learning tutorial series. In Part 1, I had shown how to put together a basic agent that learns to choose the more rewarding of two possible options. In this post, I am going to describe how we get from that simple agent to one that is capable of taking in an observation of the world, and taking actions which provide the optimal reward not just in the present, but over the long run. With these additions, we will have a full reinforcement agent. Environments which pose the full problem to an agent are referred to as Markov Decision Processes (MDPs).