Continual reinforcement learning with a multi-task agent

Abstract

A method of training an action selection neural network for controlling an agent interacting with an environment to perform different tasks is described. The method includes obtaining a first trajectory of transitions generated while the agent was performing an episode of the first task from multiple tasks; and training the action selection neural network on the first trajectory to adjust the control policies for the multiple tasks. The training includes, for each transition in the first trajectory: generating respective policy outputs for the initial observation in the transition for each task in a subset of tasks that includes the first task and one other task; generating respective target policy outputs for each task using the reward in the transition, and determining an update to the current parameter values based on, for each task, a gradient of a loss between the policy output and the target policy output for the task.