Reinforcement Learning Based Character Controlling

2020-12-14 · CUHK Course IERG5350 2020 12 · Jingbo Wang, Zijing YIN

Character controlling is a longstanding problem in understanding the behavior of human. This task aims to generate various and high quality human motion in the simulated environment as in the real world controlled by human. Therefore, how to use the captured real human motions is the crucial component to solve this problem. Rather than regressing the human motion directly in previous motion prediction methods, in this project, a reinforcement learning method is adopted in to our framework to learn robust control policies capable of imitating a broad range of example motion clips. Besides, we also explore the new reward functions to encourage the motion similarity between the real human and the virtual character. With these new rewards, the algorithm will convergence faster than recent advances.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

motion predictionreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A Survey on Reinforcement Learning Methods in Character Animation

2022-03-07 · Ariel Kwiatkowski, Eduardo Alvarado, Vicky Kalogeiton, C. Karen Liu 외

Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment. While learning, they repeatedly take…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

An Optics Controlling Environment and Reinforcement Learning Benchmarks

2021-09-29 · Abulikemu Abuduweili

Deep reinforcement learning has the potential to address various scientific problems. In this paper, we implement an optics simulation environment for reinforcement learning based controllers. The environment incorporate…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple Sarsa(λ) Reinforcement Learners

2018-06-13 · Frank G. Glavin, Michael G. Madden

This paper describes an architecture for controlling non-player characters (NPC) in the First Person Shooter (FPS) game Unreal Tournament 2004. Specifically, the DRE-Bot architecture is made up of three reinforcement lea…

Effective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you need

2023-04-05 · Colin Vignon, Jean Rabault, Joel Vasanth, Francisco Alcántara-Ávila 외

Rayleigh-B\'enard convection (RBC) is a recurrent phenomenon in several industrial and geoscience flows and a well-studied system from a fundamental fluid-mechanics viewpoint. However, controlling RBC, for example by mod…

All Deep Reinforcement Learning Multi-agent Reinforcement Learning

Controlling Rayleigh-Bénard convection via Reinforcement Learning

2020-03-31 · Gerben Beintema, Alessandro Corbetta, Luca Biferale, Federico Toschi

Thermal convection is ubiquitous in nature as well as in many industrial applications. The identification of effective control strategies to, e.g., suppress or enhance the convective heat exchange under fixed external th…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)