Search Results for author: Diego Perez-Liebana

Found 31 papers, 14 papers with code

Portfolio Search and Optimization for General Strategy Game-Playing

no code implementations21 Apr 2021 Alexander Dockhorn, Jorge Hurtado-Grueso, Dominik Jeurissen, Linjie Xu, Diego Perez-Liebana

Portfolio methods represent a simple but efficient type of action abstraction which has shown to improve the performance of search-based agents in a range of strategy games.

Learning on a Budget via Teacher Imitation

1 code implementation17 Apr 2021 Ercument Ilhan, Jeremy Gow, Diego Perez-Liebana

However, due to the realistic concerns, the number of these interactions is limited with a budget; therefore, it is crucial to perform these in the most appropriate moments.

Atari Games

Action Advising with Advice Imitation in Deep Reinforcement Learning

2 code implementations17 Apr 2021 Ercument Ilhan, Jeremy Gow, Diego Perez-Liebana

Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning.

Atari Games Behavioural cloning

Generating Diverse and Competitive Play-Styles for Strategy Games

no code implementations17 Apr 2021 Diego Perez-Liebana, Cristina Guerrero-Romero, Alexander Dockhorn, Linjie Xu, Jorge Hurtado, Dominik Jeurissen

Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games.

Decision Making

Student-Initiated Action Advising via Advice Novelty

1 code implementation1 Oct 2020 Ercument Ilhan, Jeremy Gow, Diego Perez-Liebana

Action advising is a budget-constrained knowledge exchange mechanism between teacher-student peers that can help tackle exploration and sample inefficiency problems in deep reinforcement learning (RL).

Atari Games

Design and Implementation of TAG: A Tabletop Games Framework

1 code implementation25 Sep 2020 Raluca D. Gaina, Martin Balla, Alexander Dockhorn, Raul Montoliu, Diego Perez-Liebana

This document describes the design and implementation of the Tabletop Games framework (TAG), a Java-based benchmark for developing modern board games for AI research.

Board Games TAG

The Design Of "Stratega": A General Strategy Games Framework

no code implementations11 Sep 2020 Diego Perez-Liebana, Alexander Dockhorn, Jorge Hurtado Grueso, Dominik Jeurissen

Stratega, a general strategy games framework, has been designed to foster research on computational intelligence for strategy games.

Decision Making Real-Time Strategy Games

Evaluating Generalisation in General Video Game Playing

no code implementations22 May 2020 Martin Balla, Simon M. Lucas, Diego Perez-Liebana

This paper focuses on the challenge of the GVGAI learning track in which 3 games are selected and 2 levels are given for training, while 3 hidden levels are left for evaluation.

Rolling Horizon NEAT for General Video Game Playing

no code implementations14 May 2020 Diego Perez-Liebana, Muhammad Sajid Alam, Raluca D. Gaina

This paper presents a new Statistical Forward Planning (SFP) method, Rolling Horizon NeuroEvolution of Augmenting Topologies (rhNEAT).

Guaranteeing Reproducibility in Deep Learning Competitions

no code implementations12 May 2020 Brandon Houghton, Stephanie Milani, Nicholay Topin, William Guss, Katja Hofmann, Diego Perez-Liebana, Manuela Veloso, Ruslan Salakhutdinov

To encourage the development of methods with reproducible and robust training behavior, we propose a challenge paradigm where competitors are evaluated directly on the performance of their learning procedures rather than pre-trained agents.

Rolling Horizon Evolutionary Algorithms for General Video Game Playing

1 code implementation27 Mar 2020 Raluca D. Gaina, Sam Devlin, Simon M. Lucas, Diego Perez-Liebana

Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in win rate across many video games.

Learning Local Forward Models on Unforgiving Games

1 code implementation1 Sep 2019 Alexander Dockhorn, Simon M. Lucas, Vanessa Volz, Ivan Bravi, Raluca D. Gaina, Diego Perez-Liebana

This paper examines learning approaches for forward models based on local cell transition functions.

General Video Game Rule Generation

no code implementations12 Jun 2019 Ahmed Khalifa, Michael Cerny Green, Diego Perez-Liebana, Julian Togelius

We introduce the General Video Game Rule Generation problem, and the eponymous software framework which will be used in a new track of the General Video Game AI (GVGAI) competition.

Project Thyia: A Forever Gameplayer

no code implementations10 Jun 2019 Raluca D. Gaina, Simon M. Lucas, Diego Perez-Liebana

Similarly, AI game-players are run once on a game (or maybe for longer periods of time, in the case of learning algorithms which need some, still limited, period for training), and they cease to exist once the game ends.

Ensemble Decision Systems for General Video Game Playing

no code implementations26 May 2019 Damien Anderson, Cristina Guerrero-Romero, Diego Perez-Liebana, Philip Rodgers, John Levine

Ensemble Decision Systems offer a unique form of decision making that allows a collection of algorithms to reason together about a problem.

Decision Making

Teaching on a Budget in Multi-Agent Deep Reinforcement Learning

no code implementations19 Apr 2019 Ercüment İlhan, Jeremy Gow, Diego Perez-Liebana

Deep Reinforcement Learning (RL) algorithms can solve complex sequential decision tasks successfully.

Multi-agent Reinforcement Learning

Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor

1 code implementation3 Apr 2019 Ivan Bravi, Simon Lucas, Diego Perez-Liebana, Jialin Liu

Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques.

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition

2 code implementations23 Jan 2019 Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty, Noburu Kuno, Andre Kramer, Sam Devlin, Raluca D. Gaina, Daniel Ionita

Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types.

Multi-agent Reinforcement Learning

Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best

1 code implementation3 Jan 2019 Simon M. Lucas, Jialin Liu, Ivan Bravi, Raluca D. Gaina, John Woodward, Vanessa Volz, Diego Perez-Liebana

This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation.

SMAC

Deep Reinforcement Learning for General Video Game AI

1 code implementation6 Jun 2018 Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu, Diego Perez-Liebana

In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems.

Atari Games OpenAI Gym

Shallow decision-making analysis in General Video Game Playing

1 code implementation4 Jun 2018 Ivan Bravi, Jialin Liu, Diego Perez-Liebana, Simon Lucas

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms.

Decision Making

General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms

1 code implementation28 Feb 2018 Diego Perez-Liebana, Jialin Liu, Ahmed Khalifa, Raluca D. Gaina, Julian Togelius, Simon M. Lucas

In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL).

The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation

4 code implementations16 Feb 2018 Simon M. Lucas, Jialin Liu, Diego Perez-Liebana

This paper describes the N-Tuple Bandit Evolutionary Algorithm (NTBEA), an optimisation algorithm developed for noisy and expensive discrete (combinatorial) optimisation problems.

Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

no code implementations24 Apr 2017 Raluca D. Gaina, Jialin Liu, Simon M. Lucas, Diego Perez-Liebana

Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods.

Evaluating and Modelling Hanabi-Playing Agents

no code implementations24 Apr 2017 Joseph Walton-Rivers, Piers R. Williams, Richard Bartle, Diego Perez-Liebana, Simon M. Lucas

Agent modelling involves considering how other agents will behave, in order to influence your own actions.

Game of Hanabi

General Video Game AI: Learning from Screen Capture

no code implementations23 Apr 2017 Kamolwan Kunanusont, Simon M. Lucas, Diego Perez-Liebana

General Video Game Artificial Intelligence is a general game playing framework for Artificial General Intelligence research in the video-games domain.

Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing

no code implementations23 Apr 2017 Rauca D. Gaina, Simon M. Lucas, Diego Perez-Liebana

While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative.

Evolving Game Skill-Depth using General Video Game AI Agents

no code implementations18 Mar 2017 Jialin Liu, Julian Togelius, Diego Perez-Liebana, Simon M. Lucas

The space of possible parameter settings can be seen as a search space, and we can therefore use a Random Mutation Hill Climbing algorithm or other search methods to find the parameter settings that induce the best games.

The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement

2 code implementations18 Mar 2017 Kamolwan Kunanusont, Raluca D. Gaina, Jialin Liu, Diego Perez-Liebana, Simon M. Lucas

This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design.

Ms. Pac-Man Versus Ghost Team CIG 2016 Competition

no code implementations8 Sep 2016 Piers R. Williams, Diego Perez-Liebana, Simon M. Lucas

This paper introduces the revival of the popular Ms. Pac-Man Versus Ghost Team competition.

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