The modeling of ideal transition function inI2Q isfully decentralized and independent from the learned policies of other agents, helping I2Q be free from non-stationarity and learn the optimal policy.

Fully decentralized multi-agent reinforcement learning has shown great potentials for many real-world cooperative tasks, where the global information, \textit{e.g.}, the actions of other agents, is not accessible. Although independent Q-learning is widely used for decentralized training, the transition probabilities are non-stationary since other agents are updating policies simultaneously, which leads to non-guaranteed convergence of independent Q-learning. To deal with non-stationarity, we first introduce stationary ideal transition probabilities, on which independent Q-learning could converge to the global optimum. Further, we propose a fully decentralized method, I2Q, which performs independent Q-learning on the modeled ideal transition function to reach the global optimum. The modeling of ideal transition function in I2Q is fully decentralized and independent from the learned policies of other agents, helping I2Q be free from non-stationarity and learn the optimal policy. Empirically, we show that I2Q can achieve remarkable improvement in a variety of cooperative multi-agent tasks.

Fully decentralized multi-agent reinforcement learning has shown great potentials for many real-world cooperative tasks, where the global information, \textit{e.g.}, the actions of other agents, is not accessible. Although independent Q-learning is widely used for decentralized training, the transition probabilities are non-stationary since other agents are updating policies simultaneously, which leads to non-guaranteed convergence of independent Q-learning. To deal with non-stationarity, we first introduce stationary ideal transition probabilities, on which independent Q-learning could converge to the global optimum. Further, we propose a fully decentralized method, I2Q, which performs independent Q-learning on the modeled ideal transition function to reach the global optimum. The modeling of ideal transition function in I2Q is fully decentralized and independent from the learned policies of other agents, helping I2Q be free from non-stationarity and learn the optimal policy.

Multi-Agent Reinforcement Learning involves agents that learn together in a shared environment, leading to emergent dynamics sensitive to initial conditions and parameter variations. A Dynamical Systems approach, which studies the evolution of multi-component systems over time, has uncovered some of the underlying dynamics by constructing deterministic approximation models of stochastic algorithms. In this work, we demonstrate that even in the simplest case of independent Q-learning with a Boltzmann exploration policy, significant discrepancies arise between the actual algorithm and previous approximations. We elaborate why these models actually approximate interesting variants rather than the original incremental algorithm. To explain the discrepancies, we introduce a new discrete-time approximation model that explicitly accounts for agents' update frequencies within the learning process and show that its dynamics fundamentally differ from the simplified dynamics of prior models. We illustrate the usefulness of our approach by applying it to the question of spontaneous cooperation in social dilemmas, specifically the Prisoner's Dilemma as the simplest case study. We identify conditions under which the learning behaviour appears as long-term stable cooperation from an external perspective. However, our model shows that this behaviour is merely a metastable transient phase and not a true equilibrium, making it exploitable. We further exemplify how specific parameter settings can significantly exacerbate the moving target problem in independent learning. Through a systematic analysis of our model, we show that increasing the discount factor induces oscillations, preventing convergence to a joint policy. These oscillations arise from a supercritical Neimark-Sacker bifurcation, which transforms the unique stable fixed point into an unstable focus surrounded by a stable limit cycle.

Traditional multi-agent reinforcement learning (MARL) algorithms, such as independent Q-learning, struggle when presented with partially observable scenarios, and where agents are required to develop delicate action sequences. This is often the result of the reward for a good action only being available after other agents have taken theirs, and these actions are not credited accordingly. Recurrent neural networks have proven to be a viable solution strategy for solving these types of problems, resulting in significant performance increase when compared to other methods. In this paper, we explore a different approach and focus on the experiences used to update the action-value functions of each agent. We introduce the concept of credit-cognisant rewards (CCRs), which allows an agent to perceive the effect its actions had on the environment as well as on its co-agents. We show that by manipulating these experiences and constructing the reward contained within them to include the rewards received by all the agents within the same action sequence, we are able to improve significantly on the performance of independent deep Q-learning as well as deep recurrent Q-learning. We evaluate and test the performance of CCRs when applied to deep reinforcement learning techniques at the hands of a simplified version of the popular card game Hanabi.