Markov flow policy -- deep MC
<|reference_start|>Markov flow policy -- deep MC: Discounted algorithms often encounter evaluation errors due to their reliance on short-term estimations, which can impede their efficacy in addressing simple, short-term tasks and impose undesired temporal discounts (\(\gamma\)). Interestingly, these algorithms are often tested without applying a discount, a phenomenon we refer as the \textit{train-test bias}. In response to these challenges, we propose the Markov Flow Policy, which utilizes a non-negative neural network flow to enable comprehensive forward-view predictions. Through integration into the TD7 codebase and evaluation using the MuJoCo benchmark, we observe significant performance improvements, positioning MFP as a straightforward, practical, and easily implementable solution within the domain of average rewards algorithms.<|reference_end|>
@article{soffair2024markov,
    title={Markov flow policy -- deep MC},
    author={Nitsan Soffair, Gilad Katz},
    journal={arXiv preprint arXiv:2405.00877},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00877},
    primaryClass={cs.LG cs.AI}
}