A Task Specification Language for Bootstrap Learning (Extended Abstract)

TL;DRAbstract

Traditionally, research in the reinforcement learning (RL) community has been devoted to developing domain-independent algorithms such as SARSA [13], Q-learning [16], prioritized sweeping [8], or LSPI [6], that are designed to work for any given state space and action space. However, the modus operandi in RL research has been for a human expert to re-code each learning environment, including defining the actions and state features, as well as specifying the algorithm to be used. Typically each new RL experiment is run by explicitly calling a new program (even when learning can be biased by previous learning experiences, as in transfer learning [10, 15, 14]). Thus, while standards have developed for describing and testing individual RL algorithms (e.g., RL-Glue [17]), no such standards have developed for the problem of describing complete tasks to a preexisting agent. In this paper we present a new language for specifying complete tasks, and a framework for agents to learn a new policy

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