3 citations · 3 across the 3 of their papers we have counts for
3 papers
MANSA: Learning Fast and Slow in Multi-Agent Systems
David Mguni, Haojun Chen, Taher Jafferjee +7
In multi-agent reinforcement learning (MARL), independent learning (IL) often shows remarkable performance and easily scales with the number of agents. Yet, using IL can be ineffic…
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
David Henry Mguni, Taher Jafferjee, Jianhong Wang +7
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to joint…
Learning to Shape Rewards using a Game of Two Partners
David Mguni, Taher Jafferjee, Jianhong Wang +9
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. However, RS typically relies on manually engi…