22 citations · 76 across the 14 of their papers we have counts for
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cs.LG2020★ 14 cited
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Georgios Papoudakis, Filippos Christianos, Lukas Schäfer +1
Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult. In this work, we…
cs.MA2020★ 22 cited
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by…