Reinforcement Learning (RL) provides a framework for sequential decision making under explicit objectives. In its classical form, RL studies how an agent should act to maximize long-term reward in a dynamic environment.
In richer settings, the problem extends beyond a single agent and fixed environment: intelligent behavior may require strategic interaction, adaptation to uncertainty, and reasoning over high-dimensional worlds.
This thesis studies RL from two perspectives: algorithms in games and RL in the era of foundation models. The first part focuses on multi-agent RL in games, examining how incentives, policies, and equilibrium concepts interact in competitive and general-sum environments, including two-player zero-sum games, large-scale video games, and multi-player settings with general structure.
These works investigate learning in multi-agent systems and the behavior of RL methods in interactive environments.
The second part studies RL with generative and foundation models, motivated by the idea that prior knowledge can enrich sequential decision making. Pretrained generative models and learned world models serve as representation tools and structured priors for planning, control, and policy optimization.
The thesis develops diffusion-based world models, investigates RL for efficient video generation, explores generative models as policy classes, and studies interactive video world models where actions shape future observations. It also addresses long-horizon modeling through architectures with memory.
Together, these contributions present a unified view of RL as objective-driven adaptation in complex sequential domains. From strategic games to generative world models, the thesis highlights how RL connects decision making, environment modeling, and emerging foundation-model capabilities, offering a broader perspective on the principles underlying intelligent behavior.
Blogger's Review: This paper provides an in-depth exploration of reinforcement learning's applications in multi-agent systems and foundation models, showcasing its capacity for adaptation and decision-making in dynamic environments. The introduction of generative models offers new solutions to complex problems.