• Corpus ID: 195346174

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

@article{Sherstan2018DirectlyET,
  title={Directly Estimating the Variance of the $\lambda$-Return Using Temporal-Difference Methods},
  author={Craig Sherstan and Brendan Bennett and K. Young and Dylan R. Ashley and Adam White and Martha White and Richard S. Sutton},
  journal={ArXiv},
  year={2018},
  volume={abs/1801.08287}
}
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). These values can be straightforwardly estimated by averaging batches of returns using Monte Carlo methods. However, if we wish to update the… 
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