27 citations · 48 across the 5 of their papers we have counts for
6 papers · 1 filter
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance
Marc Brittain, Xuxi Yang, Peng Wei
A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynam…
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach
Marc Brittain, Peng Wei
Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controll…
Prioritized Sequence Experience Replay
Marc Brittain, Josh Bertram, Xuxi Yang +1
Experience replay is widely used in deep reinforcement learning algorithms and allows agents to remember and learn from experiences from the past. In an effort to learn more effici…
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…
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…