Dmsh is a new multi-agent RL framework that formulates mesh generation as an MDP and uses three coordinated agents plus curriculum learning to produce globally conforming all-quad meshes without post-processing.
Discrete and Continuous Action Representation for Practical RL in Video Games
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
While most current research in Reinforcement Learning (RL) focuses on improving the performance of the algorithms in controlled environments, the use of RL under constraints like those met in the video game industry is rarely studied. Operating under such constraints, we propose Hybrid SAC, an extension of the Soft Actor-Critic algorithm able to handle discrete, continuous and parameterized actions in a principled way. We show that Hybrid SAC can successfully solve a highspeed driving task in one of our games, and is competitive with the state-of-the-art on parameterized actions benchmark tasks. We also explore the impact of using normalizing flows to enrich the expressiveness of the policy at minimal computational cost, and identify a potential undesired effect of SAC when used with normalizing flows, that may be addressed by optimizing a different objective.
years
2026 3representative citing papers
Comparative study of action factorization methods for hybrid action spaces across PPO/SAC/DQN finds branching dueling architectures effective and auto-regressive methods highest performing, with new VDN-PPO and PPO-MIX variants outperforming other PPO approaches.
RAID finds multiple diverse game exploits by sequentially training RL agents and masking previously discovered strategies from the reward function.
citing papers explorer
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Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
Dmsh is a new multi-agent RL framework that formulates mesh generation as an MDP and uses three coordinated agents plus curriculum learning to produce globally conforming all-quad meshes without post-processing.
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Revisiting Action Factorization for Complex Action Spaces
Comparative study of action factorization methods for hybrid action spaces across PPO/SAC/DQN finds branching dueling architectures effective and auto-regressive methods highest performing, with new VDN-PPO and PPO-MIX variants outperforming other PPO approaches.
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Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
RAID finds multiple diverse game exploits by sequentially training RL agents and masking previously discovered strategies from the reward function.