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On Learning Intrinsic Rewards for Policy Gradient Methods.
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), (2018): 4644-4654
In many sequential decision making tasks, it is challenging to design reward functions that help an RL agent efficiently learn behavior that is considered good by the agent designer. A number of different formulations of the reward-design problem have been proposed in the literature. In this paper we build on the Optimal Rewards Framework...More
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