Off-policy prediction—learning the value function for one policy from data generated while
following another policy—is one of the most challenging problems in reinforcement learning …

Artificial intelligence has made great strides in many areas lately, yet it has had
comparatively little success in general-use robotics. We believe one of the reasons for this is …

Within computational reinforcement learning, a growing body of work seeks to express an
agent's knowledge of its world through large collections of predictions. While systems that …

Game-solving approaches using reinforcement learning often entail a significant
computational cost. This arises from the necessity of training agents to play with or against a …

Constructing general knowledge by learning task-independent models of the world can help
agents solve challenging problems. However, both constructing and evaluating such models …

Of all the capabilities of natural intelligence, one of the most exceptional is the ability to
expand upon and refine knowledge of the world through subjective experience. Therefore, a …

Abstract Knowledge is central to intelligence. Intelligence can be thought of as the ability to
acquire knowledge and apply it effectively. Despite being a subject of intense interest in …

This thesis proposes three novel improvements to the Actor-Critic State-Action-Reward-State-
Action algorithm while considering potential biologically equivalent mechanisms. The …