In this paper we consider the problem of how a reinforcement learning agent that is tasked
with solving a sequence of reinforcement learning problems (a sequence of Markov decision …

Designing reinforcement learning (RL) agents is typically a difficult process that requires
numerous design iterations. Learning can fail for a multitude of reasons and standard RL …

Effective automated smart home energy control is essential for smart grid approaches to
demand response (DR). This is a multiobjective adaptive control problem because it …

Controlling a non-statically bipedal robot is challenging due to the complex dynamics and
multi-criterion optimization involved. Recent works have demonstrated the effectiveness of …

Sustainable manufacturing practices are crucial in job shop scheduling (JSS) to enhance
the resilience of production systems against resource shortages and regulatory changes …

Reinforcement learning (RL) for complex tasks remains a challenge, primarily due to the
difficulties of engineering scalar reward functions and the inherent inefficiency of training …

As the capabilities of artificial agents improve, they are being increasingly deployed to
service multiple diverse objectives and stakeholders. However, the composition of these …

Abstract In Reinforcement Learning, an intelligent agent has to make a sequence of
decisions to accomplish a goal. If this sequence is long, then the agent has to plan over a …

Multi-agent reinforcement learning requires numerous interactions with the environment and
other agents to learn an optimal policy. The teacher–student framework is one paradigm that …

Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
In reinforcement learning, rationally reusing the policies acquired from other tasks or human …