Search Results for author:
Found 4 papers, 1 papers with code
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning
In this work, we first demonstrate that sample complexity increases with the quantity of absorbing states in a toy supervised learning task for a fully connected network, while attention is more robust to variable size input.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Automatic Game Design via Mechanic Generation
We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics---what they do in the game.
Monte-Carlo Tree Search for Simulation-based Strategy Analysis
Using these case studies, we show how using simulated agents to model humans of varying skill levels allows us to extract metrics to describe game balance (in the case of Scrabble) and highlight potential design flaws (in the case of Cardonomicon).
Automatic Playtesting for Game Parameter Tuning via Active Learning
Through a case study on a shoot-`em-up game we demonstrate the efficacy of active learning to reduce the amount of playtesting needed to choose the optimal set of game parameters for two classes of (formal) design objectives.
