ePoster · Reinforcement Learning
Using Markov Decision Processes to benchmark the performance of artificial and biological agents
Alexander Kazakov,Ana Polterovich,Maciej M. Jankowski,Johannes Niediek,Israel Nelken · COSYNE 2022
Thu, Mar 17, 2022
When an agent is trained on a complex episodic task, different task parts may be learned at different rates. How do we determine which part of the task challenged the agent the most? Since reward is provided usually only at the end of each trial, it cannot be used to infer within-trial learning trends. Behavioral features such as speed or trial duration capture trends in the agent's decision-making, but do not necessarily indicate that the agent is getting better at the task. We address this issue by modeling the task as a Markov Decision Process (MDP). The Q values of the optimal policy measu