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This project implements Q-Learning to find the optimal policy for charging and discharging electric vehicles in a V2G scheme under conditions of uncertain commitment of EV owners. The problem is modelled as a multi-objective multi-agent cooperative game. Project is part of fulfillment criteria for ECE 730 course at the University of Alberta.
Multi-Agent Deep RL meets Darwinian evolution: predator/prey agents learn under sparse reproduction-only rewards (no shaping, no cooperation signal) while heritable traits mutate and are selected across generations — a live testbed for the Baldwin effect, and for whether cooperation, defection, and free-riding emerge unassisted.