no code implementations • 6 Jul 2018 • Tanner Fiez, Shreyas Sekar, Liyuan Zheng, Lillian J. Ratliff
The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge.
no code implementations • 26 Jan 2020 • Liyuan Zheng, Lillian J. Ratliff
Constrained Markov Decision Processes are a class of stochastic decision problems in which the decision maker must select a policy that satisfies auxiliary cost constraints.
1 code implementation • 20 Mar 2020 • Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff, Baosen Zhang
This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints.
no code implementations • L4DC 2020 • Liyuan Zheng, Lillian Ratliff
Constrained Markov Decision Processes are a class of stochastic decision problems in which the decision maker must select a policy that satisfies auxiliary cost constraints.
1 code implementation • 25 Sep 2021 • Liyuan Zheng, Tanner Fiez, Zane Alumbaugh, Benjamin Chasnov, Lillian J. Ratliff
The hierarchical interaction between the actor and critic in actor-critic based reinforcement learning algorithms naturally lends itself to a game-theoretic interpretation.
no code implementations • 4 May 2023 • Boling Yang, Liyuan Zheng, Lillian J. Ratliff, Byron Boots, Joshua R. Smith
Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving scheme.