In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

While multi-agent trust region algorithms have achieved great success empirically in solving
coordination tasks, most of them, however, suffer from a non-stationarity problem since …

Recent developments in reinforcement learning (RL) have been able to derive optimal
policies for sophisticated and capable agents, and shown to achieve human-level …

The safe and efficient navigation of autonomous vehicles in complex traffic scenarios
remains a significant challenge. One of the key impediments is the limited effectiveness of …

In recent years, cooperative multiagent deep reinforcement learning (MADRL) has received
increasing research interest and has been widely applied to computer games and …

We present trust region bounds for optimizing decentralized policies in cooperative Multi-
Agent Reinforcement Learning (MARL), which holds even when the transition dynamics are …

While Centralized Training with Decentralized Execution (CTDE) has become the prevailing
paradigm in Multi-Agent Reinforcement Learning (MARL), it may not be suitable for …

Trust region methods are widely applied in single-agent reinforcement learning problems
due to their monotonic performance-improvement guarantee at every iteration. Nonetheless …

We present a new monotonic improvement guarantee for optimizing decentralized policies
in cooperative Multi-Agent Reinforcement Learning (MARL), which holds even when the …