  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.
