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Found 8 papers, 5 papers with code
Evolutionary Reinforcement Learning via Cooperative Coevolution
Maintaining a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient exploration.
Generating Games via LLMs: An Investigation with Video Game Description Language
Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation.
Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks
In this work, we propose a universal foundation model for medical image analysis that processes images with heterogeneous spatial properties using a unified structure.
Generating Redstone Style Cities in Minecraft
Procedurally generating cities in Minecraft provides players more diverse scenarios and could help understand and improve the design of cities in other digital worlds and the real world.
Constrained Reinforcement Learning for Dynamic Material Handling
Experimental results on the problem instances demonstrate the outstanding performance of our proposed approach compared with eight state-of-the-art constrained and non-constrained reinforcement learning algorithms, and widely used dispatching rules for material handling.
Game-based Platforms for Artificial Intelligence Research
Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios.
Evolving Constrained Reinforcement Learning Policy
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 temporal credit assignment problem and improves the exploration efficiency.
Reinforcement Learning with Dual-Observation for General Video Game Playing
Our proposed technique is implemented with three state-of-the-art reinforcement learning algorithms and tested on the game set of the 2020 General Video Game AI Learning Competition.
