Search Results for author:
Found 36 papers, 18 papers with code
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency.
Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents
In contrast, agents tested in dynamic robot environments face limitations due to simplistic environments with only a few objects and interactions.
PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games
To bridge this gap, we introduce PyTAG, a Python API for interacting with the Tabletop Games framework (TAG).
Task Relabelling for Multi-task Transfer using Successor Features
Successor Features (SFs) proposes a mechanism that allows learning policies that are not tied to any particular reward function.
Visualising Multiplayer Game Spaces
We compare four different `game-spaces' in terms of their usefulness in characterising multi-player tabletop games, with a particular interest in any underlying change to a game's characteristics as the number of players changes.
Portfolio Search and Optimization for General Strategy Game-Playing
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
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.
Action Advising with Advice Imitation in Deep Reinforcement Learning
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.
Generating Diverse and Competitive Play-Styles for Strategy Games
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.
Student-Initiated Action Advising via Advice Novelty
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).
Design and Implementation of TAG: A Tabletop Games Framework
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.
The Design Of "Stratega": A General Strategy Games Framework
Stratega, a general strategy games framework, has been designed to foster research on computational intelligence for strategy games.
Evaluating Generalisation in General Video Game Playing
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
This paper presents a new Statistical Forward Planning (SFP) method, Rolling Horizon NeuroEvolution of Augmenting Topologies (rhNEAT).
Guaranteeing Reproducibility in Deep Learning Competitions
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
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
This paper examines learning approaches for forward models based on local cell transition functions.
General Video Game Rule Generation
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
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
Ensemble Decision Systems offer a unique form of decision making that allows a collection of algorithms to reason together about a problem.
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning
Deep Reinforcement Learning (RL) algorithms can solve complex sequential decision tasks successfully.
Multi-agent Reinforcement Learning
reinforcement-learning
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Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor
Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques.
A Local Approach to Forward Model Learning: Results on the Game of Life Game
This paper investigates the effect of learning a forward model on the performance of a statistical forward planning agent.
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
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
reinforcement-learning
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Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
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.
Deep Reinforcement Learning for General Video Game AI
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.
Shallow decision-making analysis in General Video Game Playing
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.
General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms
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
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
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
Agent modelling involves considering how other agents will behave, in order to influence your own actions.
General Video Game AI: Learning from Screen Capture
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
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
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
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
This paper introduces the revival of the popular Ms. Pac-Man Versus Ghost Team competition.
