Reinforcement-learning agents with different temperature parameters explain the variety of human actionselection behavior in a Markov decision process task
We investigated the characteristics of the human actionselection in performing a Markov decision process (MDP) task, and compared them to those of reinforcement-learning (RL) agents. The behavior of human participants was roughly classified into two qualitatively different types. On the other hand, surprisingly, the variety of human behavior could be explained simply by a single parameter of the degree of randomness (i.e., the temperature parameter) in the actionselection rules of the RL agents. This result implies that the various behaviors of human actionselection may be determined by a simple mechanism in the brain.
markov decision process human behavior action selection reinforcement learning inverse temperature parameter