12 citations · 20 across the 6 of their papers we have counts for
3 papers · 1 filter
On Convex Optimal Value Functions For POSGs
Rafael F. Cunha, Jacopo Castellini, Johan Peralez +1
Multi-agent planning and reinforcement learning can be challenging when agents cannot see the state of the world or communicate with each other due to communication costs, latency,…
Difference Rewards Policy Gradients
Jacopo Castellini, Sam Devlin, Frans A. Oliehoek +1
Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of…
Analysing Factorizations of Action-Value Networks for Cooperative Multi-Agent Reinforcement Learning
Jacopo Castellini, Frans A. Oliehoek, Rahul Savani +1
Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theore…