Deep Reinforcement Learning with an Unbounded Action Space. (arXiv:1511.04636v3 [cs.AI] UPDATED)

In this paper, we propose the deep reinforcement relevance network (DRRN), a novel deep architecture, to design a better model for handling an unbounded action space with applications to language understanding for text-based games. For a particular class of games, a user must choose among a variable number of actions described by text, with the goal of maximizing long-term reward. In these games, the best action is typically that which best fits to the current situation (modeled as a state in the DRRN), also described by text. Because of the exponential complexity of natural language with respect to sentence length, there is typically an unbounded set of unique actions. Therefore, it is difficult to pre-define the action set. To address this challenge, the DRRN extracts separate high-level embedding vectors from the texts that describe states and actions, respectively, using a general interaction function, exploring inner product, bilinear, and DNN interaction, between these embedding vectors to approximate the Q-function. We evaluate the DRRN on two popular text games, showing superior performance over other deep Q-learning architectures.

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