While recent work in reinforcement learning (RL) has led to agents capable of solving
increasingly complex tasks, the issue of high sample complexity is still a major concern. This …

Transfer learning in reinforcement learning has been an active area of research over the
past decade. In transfer learning, training on a source task is leveraged to speed up or …

Collective human knowledge has clearly benefited from the fact that innovations by
individuals are taught to others through communication. Similar to human social groups …

Providing Reinforcement Learning agents with expert advice can dramatically improve
various aspects of learning. Prior work has developed teaching protocols that enable agents …

Multi-agent systems can be used to address problems in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …

A long-term goal of reinforcement learning agents is to be able to perform tasks in complex
real-world scenarios. The use of external information is one way of scaling agents to more …

As robots become pervasive in human environments, it is important to enable users to
effectively convey new skills without programming. Most existing work on Interactive …

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through
their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the …

When deploying autonomous agents in the real world, we need effective ways of
communicating objectives to them. Traditional skill learning has revolved around …

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …