ItzelOlivos/GOAL-CR

This project is concerned with computing channel access strategies that minimize the expected contention resolution time in single-hop random access networks. The uncertainty in the contention is addressed by modeling the problem as a partially observable stochastic game. A Reinforcement Learning method is implemented to find approximately optimal solutions. In addition, a novel algorithm was developed to compute optimal strategies in more efficient running time.

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