Sample-Efficient Multi-Task and Multi-Objective Reinforcement Learning by Combining Multiple Behaviors
New multi-policy methods that empower RL agents to carefully learn multiple behaviors, each specialized in a particular task; and combine previously-learned behaviors to efficiently identify solutions to novel tasks, which, importantly, may require the agent to assign different preferences to each of its new objectives...