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Found 10 papers, 5 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.
AutoRL Hyperparameter Landscapes
Addressing an important open question on the legitimacy of such dynamic AutoRL approaches, we provide thorough empirical evidence that the hyperparameter landscapes strongly vary over time across representative algorithms from RL literature (DQN, PPO, and SAC) in different kinds of environments (Cartpole, Bipedal Walker, and Hopper) This supports the theory that hyperparameters should be dynamically adjusted during training and shows the potential for more insights on AutoRL problems that can be gained through landscape analyses.
Hyperparameter Optimization
Open-Ended Question Answering
+1
Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game Playing
In this paper, we propose Elastic MCTS, an algorithm that uses state abstraction to play strategy games.
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.
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.
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.
Learning Local Forward Models on Unforgiving Games
This paper examines learning approaches for forward models based on local cell transition functions.
Introducing the Hearthstone-AI Competition
The Hearthstone AI framework and competition motivates the development of artificial intelligence agents that can play collectible card games.
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.
