Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods

25 Jan 2018Craig SherstanBrendan BennettKenny YoungDylan R. AshleyAdam WhiteMartha WhiteRichard S. Sutton

This paper investigates estimating the variance of a temporal-difference learning agent's update target. Most reinforcement learning methods use an estimate of the value function, which captures how good it is for the agent to be in a particular state and is mathematically expressed as the expected sum of discounted future rewards (called the return)... (read more)

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