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We propose a multi-agent distributed reinforcement learning algorithm thatbalances between potentially conflicting short-term reward and sparse, delayedlong-term reward, and learns with partial information in a dynamic environment.We compare different long-term rewards to incentivize the algorithm to maximizeindividual payoff and overall social welfare. We test the algorithm in twosimulated auction games, and demonstrate that 1) our algorithm outperforms twobenchmark algorithms in a direct competition, with cost to social welfare, and2) our algorithm's aggressive competitive behavior can be guided with thelong-term reward signal to maximize both individual payoff and overall socialwelfare.

Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction Games