Search Results for author: Liyuan Zheng

Found 6 papers, 2 papers with code

Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula

no code implementations4 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.

Multi-agent Reinforcement Learning

Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms

1 code implementation25 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.

OpenAI Gym reinforcement-learning +1

Constrained Upper Confidence Reinforcement Learning with Known Dynamics

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.

reinforcement-learning Reinforcement Learning (RL)

Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks

1 code implementation20 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.

reinforcement-learning Reinforcement Learning (RL) +1

Constrained Upper Confidence Reinforcement Learning

no code implementations26 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.

reinforcement-learning Reinforcement Learning (RL)

Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences

no code implementations6 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.

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