33 citations · 124 across the 19 of their papers we have counts for
4 papers · 1 filter
Explainable Deterministic MDPs
Josh Bertram, Peng Wei
We present a method for a certain class of Markov Decision Processes (MDPs) that can relate the optimal policy back to one or more reward sources in the environment. For a given in…
Hierarchical Reinforcement Learning with Deep Nested Agents
Marc Brittain, Peng Wei
Deep hierarchical reinforcement learning has gained a lot of attention in recent years due to its ability to produce state-of-the-art results in challenging environments where non-…
Memoryless Exact Solutions for Deterministic MDPs with Sparse Rewards
Joshua R. Bertram, Peng Wei
We propose an algorithm for deterministic continuous Markov Decision Processes with sparse rewards that computes the optimal policy exactly with no dependency on the size of the st…
Fast Online Exact Solutions for Deterministic MDPs with Sparse Rewards
Joshua R. Bertram, Xuxi Yang, Peng Wei
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision making under uncertainty. The classical approaches for solving MDPs are well known an…