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Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
[article]
2020
arXiv
pre-print
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged
arXiv:2003.04960v2
fatcat:iacmqeb7jjeezpo27jsnzuqb7u