Time Adaptive Reinforcement Learning

2020-04-18 · Chris Reinke

Reinforcement learning (RL) allows to solve complex tasks such as Go often with a stronger performance than humans. However, the learned behaviors are usually fixed to specific tasks and unable to adapt to different contexts. Here we consider the case of adapting RL agents to different time restrictions, such as finishing a task with a given time limit that might change from one task execution to the next. We define such problems as Time Adaptive Markov Decision Processes and introduce two model-free, value-based algorithms: the Independent Gamma-Ensemble and the n-Step Ensemble. In difference to classical approaches, they allow a zero-shot adaptation between different time restrictions. The proposed approaches represent general mechanisms to handle time adaptive tasks making them compatible with many existing RL methods, algorithms, and scenarios.

📄 PDF Abstract BibTeX arXiv:2004.08600

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays

2023-05-26 · Luc McCutcheon, Saber Fallah

Local-remote systems allow robots to execute complex tasks in hazardous environments such as space and nuclear power stations. However, establishing accurate positional mapping between local and remote devices can be dif…

Deep Reinforcement Learning Model-based Reinforcement Learning reinforcement-learning Reinforcement Learning

Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation

2019-05-30 · Byung Hoon Ahn, Prannoy Pilligundla, Hadi Esmaeilzadeh

Achieving faster execution with shorter compilation time can enable further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized librarie…

Clustering Diversity reinforcement-learning Reinforcement Learning+1

Online Reinforcement Learning-Based Dynamic Adaptive Evaluation Function for Real-Time Strategy Tasks

2025-01-07 · Weilong Yang, Jie Zhang, Xunyun Liu, Yanqing Ye

Effective evaluation of real-time strategy tasks requires adaptive mechanisms to cope with dynamic and unpredictable environments. This study proposes a method to improve evaluation functions for real-time responsiveness…

reinforcement-learning Reinforcement Learning

Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems

2022-10-12 · Felix Feit, Andreas Metzger, Klaus Pohl

Design time uncertainty poses an important challenge when developing a self-adaptive system. As an example, defining how the system should adapt when facing a new environment state, requires understanding the precise eff…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Self Adaptive System

An Online Model-Following Projection Mechanism Using Reinforcement Learning

2023-02-05 · Mohammed I. Abouheaf, Hashim A. Hashim, Mohammad A. Mayyas, Kyriakos G. Vamvoudakis

In this paper, we propose a model-free adaptive learning solution for a model-following control problem. This approach employs policy iteration, to find an optimal adaptive control solution. It utilizes a moving finite-h…