Topic: meta-reinforcement learning

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmente

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmente

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. This flexibility has been suggested to result from fast recurrent dynamics in the prefrontal network, acquired through a process of meta-reinforcement learning (Wang et al., 2018). However, these models fail to capture an important aspect of human behavior: when adapting to new information, we spend variable and often long periods of time contemplating possible futures before acting. For such planning to be rational, its benefits to future behavior must at least compensate for the