deep-rl

Value-Decomposition Networks for Cooperative Multi-Agent Learning

We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large combined action and observation spaces. In the fully centralized and decentralized approaches, we find...

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning

policies during the training procedure. This non-stationarity stems from breaking the Markov assumption that governs most Recent developments in deep reinforcement learning single-agent RL algorithms. Since the transitions and rewards are concerned with creating decision-making a...

Deep Reinforcement Learning for Multi-Agent Interaction

. The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Towards this goal, the Autonomous Agents Research Group develops novel machine learning algorithms fo...

Simplifying Deep Temporal Difference Learning