Reinforcement learning (RL) methods usually treat reward functions as black boxes. As
such, these methods must extensively interact with the environment in order to discover …

We address the problem of teaching a deep reinforcement learning (RL) agent to follow
instructions in multi-task environments. Instructions are expressed in a well-known formal …

Reinforcement Learning (RL) has achieved tremendous development in recent years, but
still faces significant obstacles in addressing complex real-life problems due to the issues of …

It is an interesting open problem to enable robots to efficiently and effectively learn long-
horizon manipulation skills. Motivated to augment robot learning via more effective …

Text-based games present a unique class of sequential decision making problem in which
agents interact with a partially observable, simulated environment via actions and …

Continuously learning new tasks using high-level ideas or knowledge is a key capability of
humans. In this paper, we propose lifelong reinforcement learning with sequential linear …

Reinforcement learning (RL) with linear temporal logic (LTL) objectives can allow robots to
carry out symbolic event plans in unknown environments. Most existing methods assume …

Reward machines (RMs) are a recent formalism for representing the reward function of a
reinforcement learning task through a finite-state machine whose edges encode subgoals of …

Deploying robots in real-world domains, such as households and flexible manufacturing
lines, requires the robots to be taskable on demand. Linear temporal logic (LTL) is a widely …

Temporal logic (TL) tasks consist of complex and temporally extended subgoals and they
are common for many real-world applications, such as service and navigation robots …