Topic: deep Q-network

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

A common way of quantifying learning behavior is the estimation of learning rates from subjects’ responses. To this end responses are usually averaged across subjects when estimating learning rates for a whole group or averaged over a sliding window when estimating learning rates for single subjects. However, these methods are difficult to apply when the number of experimental subjects and/or the number of trials are too low. Furthermore, the process of averaging obscures the actual dynamics of learning, which can occur within few trials. Indeed, we found that for previous data from a contextu