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Directly Estimating the Variance of the {\lambda}-Return Using Temporal-Difference Methods

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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 (call...More

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Craig Sherstan
Craig Sherstan
Brendan Bennett
Brendan Bennett
Dylan R. Ashley
Dylan R. Ashley
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