Developing　optimal　bidding　strategies　for　the　market　participants　plays　a　crucial　role　in　increasing　the　profit　of　the　electricity　market.　For　the　complex　double-sided　auction　market　featured　with　partial　observability,　uncertainty　and　dynamic　nature　in　the　bidding　transaction,　current　methods　like　Reinforcement　Learning　(RL)　based　on　single　agent　framework　are　difficult　to　model　the　strategic　bidding　behavior　of　both　supply　side　and　demand　side　with　elastic　demand.　For　this　purpose,　this　paper　proposes　a　multi-agent　system　based　on　Multi-Agent　Deep　Deterministic　Policy　Gradient　(MADDPG),　combined　with　Prioritized　Experience　Replay　(PER)　mechanism,　namely　MADDPG-PER,　to　simulate　the　complex　market　environment.　The　multi-agent　system　based　on　MADDPG-PER　is　not　only　able　to　deal　with　the　sparse　reward　gradient　phenomenon　for　auction-based　electricity　market,　but　also　can　find　superior　bidding　strategies　for　the　market　participants.　The　MADDPG-PER　algorithm　is　evaluated　on　9-　bus　and　30-　bus　congested　network,　where　both　supply　and　demand　sides　are　modeled　as　RL　agents.　The　results　demonstrate　that　MADDPG-PER　outperforms　the　state-of-the-art　methods　under　different　level　of　system　uncertainty.　IEEE