Search Results for author: Richard Dazeley

Found 14 papers, 1 papers with code

A Broad-persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments

no code implementations15 Oct 2021 Hung Son Nguyen, Francisco Cruz, Richard Dazeley

However, current research has been limited to interactions that offer actionable advice to only the current state of the agent.

Robot Navigation

Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey

no code implementations20 Aug 2021 Richard Dazeley, Peter Vamplew, Francisco Cruz

EXplainable RL (XRL) is relatively recent field of research that aims to develop techniques to extract concepts from the agent's: perception of the environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and objectives.

Decision Making Explainable artificial intelligence

Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task

no code implementations18 Aug 2021 Angel Ayala, Francisco Cruz, Bruno Fernandes, Richard Dazeley

Explainable reinforcement learning allows artificial agents to explain their behavior in a human-like manner aiming at non-expert end-users.

Decision Making

Levels of explainable artificial intelligence for human-aligned conversational explanations

no code implementations7 Jul 2021 Richard Dazeley, Peter Vamplew, Cameron Foale, Charlotte Young, Sunil Aryal, Francisco Cruz

Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML).

Decision Making Explainable artificial intelligence +1

Persistent Rule-based Interactive Reinforcement Learning

no code implementations4 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.

Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning

no code implementations21 Sep 2020 Adam Bignold, Francisco Cruz, Richard Dazeley, Peter Vamplew, Cameron Foale

To address this issue, interactive reinforcement learning proposes the use of externally-sourced information in order to speed up the learning process.

Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment

no code implementations7 Jul 2020 Ithan Moreira, Javier Rivas, Francisco Cruz, Richard Dazeley, Angel Ayala, Bruno Fernandes

We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL).

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

no code implementations3 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 Transfer Learning

Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario

no code implementations24 Jun 2020 Francisco Cruz, Richard Dazeley, Peter Vamplew, Ithan Moreira

As a way to explain the goal-driven robot's actions, we use the probability of success computed by three different proposed approaches: memory-based, learning-based, and introspection-based.

Action Understanding Decision Making

Discrete-to-Deep Supervised Policy Learning

1 code implementation5 May 2020 Budi Kurniawan, Peter Vamplew, Michael Papasimeon, Richard Dazeley, Cameron Foale

It then selects from each discrete state an input value and the action with the highest numerical preference as an input/target pair.

A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions

no code implementations14 Apr 2020 Peter Vamplew, Cameron Foale, Richard Dazeley

We report a previously unidentified issue with model-free, value-based approaches to multiobjective reinforcement learning in the context of environments with stochastic state transitions.

A Multi-Objective Deep Reinforcement Learning Framework

no code implementations8 Mar 2018 Thanh Thi Nguyen, Ngoc Duy Nguyen, Peter Vamplew, Saeid Nahavandi, Richard Dazeley, Chee Peng Lim

This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks.

A Survey of Multi-Objective Sequential Decision-Making

no code implementations4 Feb 2014 Diederik Marijn Roijers, Peter Vamplew, Shimon Whiteson, Richard Dazeley

Using this taxonomy, we survey the literature on multi-objective methods for planning and learning.

Decision Making

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