On the Reliability and Generalizability of Brain-inspired Reinforcement Learning Algorithms

Although deep RL models have shown a great potential for solving various types of tasks with minimal supervision, several key challenges remain in terms of learning rapidly from limited experience, adapting to environmental changes, and generalizing learning from a single task. Recent evidence in decision neuroscience has shown that the human brain has an innate capacity to resolve these issues, leading to optimism regarding the development of neuroscience-inspired solutions toward sample-efficient, adaptive, and generalizable RL algorithms. We show that the computational model, adaptively combining model-based and model-free control, which we term the prefrontal RL, reliably encodes the information of highlevel policy that humans learned, and this model can generalize the learned policy to a wide range of tasks. First, we trained the prefrontal RL, deep RL, and meta RL algorithms on 82 human subjects' data, collected while human participants were performing two-stage Markov decision tasks, in which we experimentally manipulated the goal, state-transition uncertainty, and state-space complexity. In the reliability test, which is based on a combination of the latent behavior profile and the parameter recoverability test, we showed that the prefrontal RL reliably learned the latent policies of the human subjects, while all the other models failed to pass this test. Second, to empirically test the ability to generalize what these models learned from the original task, we situated them in the context of environmental volatility. Specifically, we ran large-scale simulations with 10 different Markov decision tasks, in which latent context variables change over time. Our information-theoretic analysis showed that the prefrontal RL showed the highest level of adaptability and episodic encoding efficacy. To the best of our knowledge, this is the first attempt to formally test the possibility that computational models mimicking the way the brain solves general problems can lead to practical solutions to key challenges in machine learning.