Broad-persistent Advice for Interactive Reinforcement Learning Scenarios

no code implementations • 11 Oct 2022 • Francisco Cruz, Adam Bignold, Hung Son Nguyen, Richard Dazeley, Peter Vamplew

The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents.

reinforcement-learning Reinforcement Learning (RL)

Persistent Rule-based Interactive Reinforcement Learning

no code implementations • 4 Feb 2021 • Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale

Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-time.

reinforcement-learning Reinforcement Learning (RL)

Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning

no code implementations • 21 Sep 2020 • Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale

When interacting with a learner agent, humans may provide either evaluative or informative advice.

reinforcement-learning Reinforcement Learning (RL)

A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review

no code implementations • 3 Jul 2020 • Adam Bignold, Francisco Cruz, Matthew E. Taylor, Tim Brys, Richard Dazeley, Peter Vamplew, Cameron Foale

In this work, while reviewing externally-influenced methods, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering collaboration by classifying and comparing various methods that use external information in the learning process.

Decision Making reinforcement-learning +2