Reinforcement Learning
In partially observable environments, an agent’s policy should often be a function of the history of its interaction with the …
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and …
Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In …
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we …
In Reinforcement Learning (RL), an agent is guided by the rewards it receives from the reward function. Unfortunately, it may take many …
This paper examines the problem of how to teach multiple tasks to a Reinforcement Learning (RL) agent. To this end, we use Linear …
In this paper we propose Reward Machines – a type of finite state machine that supports the spec- ification of reward functions while …
Convergence to an optimal policy using model-based rein- forcement learning can require significant exploration of the environment. In …