SEED at IEEE CoG 2021
SEED’s team presented two terrific papers at this year’s 3rd IEEE Conference on Games. Details below.
Improving Playtesting Coverage via Curiosity-Driven Reinforcement Learning Agents
As modern games continue growing both in size and complexity, it has become more challenging to ensure that all the relevant content is tested and that any potential issue is properly identified and fixed. Attempting to maximize testing coverage using only human participants, however, results in a tedious and hard to orchestrate process which normally slows down the development cycle.
Complementing playtesting via autonomous agents has shown great promise in accelerating and simplifying this process.
Authors: Camilo Gordillo, Joakim Bergdahl, Konrad Tollmar, Linus Gisslén
Adversarial Reinforcement Learning for Procedural Content Generation
Nominated for best paper at CoG 2021.
Training RL agents for generalization over novel environments is a notoriously difficult task. One popular approach is to procedurally generate different environments to increase the generalizability of the trained agents. Here, we deploy an adversarial model with one PCG RL agent (called Generator), and one solving RL agent (called Solver).
The benefit is mainly two-fold: First, the Solver achieves better generalization through the generated challenges from the Generator. Second, the trained Generator can be used as a creator of novel environments that, together with the Solver, can be shown to be solvable. The Generator receives a reward signal based on the performance of the Solver which encourages the environment design to be challenging but not impossible.
Authors: Linus Gisslén, Andy Eakins, Camilo Gordillo, Joakim Bergdahl, Konrad Tollmar
