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Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
[article]
2020
arXiv
pre-print
Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making. In this paper, we examine the problem of infusing RL agents with commonsense knowledge. Such knowledge would allow agents to efficiently act in the world by pruning out implausible actions, and to perform look-ahead planning to determine how current actions might affect future world states. We design a
arXiv:2010.03790v1
fatcat:shnfd4e3ebbazktfmhqrqi5bza