18 citations · 82 across the 21 of their papers we have counts for
25 papers
Evolving Constrained Reinforcement Learning Policy
Chengpeng Hu, Jiyuan Pei, Jialin Liu +1
Evolutionary algorithms have been used to evolve a population of actors to generate diverse experiences for training reinforcement learning agents, which helps to tackle the tempor…
Online Game Level Generation from Music
Ziqi Wang, Jialin Liu
Game consists of multiple types of content, while the harmony of different content types play an essential role in game design. However, most works on procedural content generation…
Reinforcement Learning with Dual-Observation for General Video Game Playing
Chengpeng Hu, Ziqi Wang, Tianye Shu +4
Reinforcement learning algorithms have performed well in playing challenging board and video games. More and more studies focus on improving the generalisation ability of reinforce…
Towards in-store multi-person tracking using head detection and track heatmaps
Aibek Musaev, Jiangping Wang, Liang Zhu +6
Computer vision algorithms are being implemented across a breadth of industries to enable technological innovations. In this paper, we study the problem of computer vision based cu…
Stock Prices Prediction using Deep Learning Models
Jialin Liu, Fei Chao, Yu-Chen Lin +1
Financial markets have a vital role in the development of modern society. They allow the deployment of economic resources. Changes in stock prices reflect changes in the market. In…
Gradient Boost with Convolution Neural Network for Stock Forecast
Jialin Liu, Chih-Min Lin, Fei Chao
Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contain…
Decoder Choice Network for Meta-Learning
Jialin Liu, Fei Chao, Longzhi Yang +2
Meta-learning has been widely used for implementing few-shot learning and fast model adaptation. One kind of meta-learning methods attempt to learn how to control the gradient desc…
Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor
Ivan Bravi, Simon Lucas, Diego Perez-Liebana +1
Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques. Providing diverse challenges is crucial to push research to…
Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
Simon M. Lucas, Jialin Liu, Ivan Bravi +4
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. Planet Wars…
Deep Reinforcement Learning for General Video Game AI
Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius +2
The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-spec…
Shallow decision-making analysis in General Video Game Playing
Ivan Bravi, Jialin Liu, Diego Perez-Liebana +1
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, hy…
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
Vanessa Volz, Jacob Schrum, Jialin Liu +3
Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content…
The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation
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.…
Efficient Noisy Optimisation with the Sliding Window Compact Genetic Algorithm
Simon M. Lucas, Jialin Liu, Diego Pérez-Liébana
The compact genetic algorithm is an Estimation of Distribution Algorithm for binary optimisation problems. Unlike the standard Genetic Algorithm, no cross-over or mutation is invol…
Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic
Simon M. Lucas, Jialin Liu, Diego Pérez-Liébana
A key part of any evolutionary algorithm is fitness evaluation. When fitness evaluations are corrupted by noise, as happens in many real-world problems as a consequence of various…
The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement
Kamolwan Kunanusont, Raluca D. Gaina, Jialin Liu +2
This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design. The approach adopted in this paper is to use observations of AI agents…
Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing
Raluca D. Gaina, Jialin Liu, Simon M. Lucas +1
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 ma…
Evolving Game Skill-Depth using General Video Game AI Agents
Jialin Liu, Julian Togelius, Diego Perez-Liebana +1
Most games have, or can be generalised to have, a number of parameters that may be varied in order to provide instances of games that lead to very different player experiences. The…
Automatically Reinforcing a Game AI
David L. St-Pierre, Jean-Baptiste Hoock, Jialin Liu +2
A recent research trend in Artificial Intelligence (AI) is the combination of several programs into one single, stronger, program; this is termed portfolio methods. We here investi…
Learning opening books in partially observable games: using random seeds in Phantom Go
Tristan Cazenave, Jialin Liu, Fabien Teytaud +1
Many artificial intelligences (AIs) are randomized. One can be lucky or unlucky with the random seed; we quantify this effect and show that, maybe contrarily to intuition, this is…
Rolling Horizon Coevolutionary Planning for Two-Player Video Games
Jialin Liu, Diego Pérez-Liébana, Simon M. Lucas
This paper describes a new algorithm for decision making in two-player real-time video games. As with Monte Carlo Tree Search, the algorithm can be used without heuristics and has…
Optimal resampling for the noisy OneMax problem
Jialin Liu, Michael Fairbank, Diego Pérez-Liébana +1
The OneMax problem is a standard benchmark optimisation problem for a binary search space. Recent work on applying a Bandit-Based Random Mutation Hill-Climbing algorithm to the noi…
Scenario-based decision-making for power systems investment planning
Jialin Liu, Olivier Teytaud
The optimization of power systems involves complex uncertainties, such as technological progress, political context, geopolitical constraints. Negotiations at COP21 are complicated…
Bandit-Based Random Mutation Hill-Climbing
Jialin Liu, Diego Peŕez-Liebana, Simon M. Lucas
The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains. It repeats the process of randomly selecting a neighbour of a best-so-far…