CSKrishna/Optimal-bidding-policy-using-Policy-Gradient-in-a-Multi-agent-Contextual-Bandit-setting
We use policy gradient to help agents learn optimal policies in a competitive multi-agent contextual bandit setting
This project helps energy traders or market participants in a day-ahead electricity market learn how to optimally bid for products. By analyzing market conditions like oil prices and weather, it helps determine the best price and quantity to bid to maximize profit. It takes market state information as input and provides an optimal bidding strategy as output for multiple competing agents.
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Use this if you are a market participant in a competitive market and need to develop an automated, data-driven strategy to optimize your bidding policy against other hidden competitors.
Not ideal if your market is not competitive or if you have full visibility into your competitors' actions.
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Mar 09, 2018
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