Wentse Chen
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State Combinatorial Generalization In Decision Making With Conditional Diffusion Models Open
Many real-world decision-making problems are combinatorial in nature, where states (e.g., surrounding traffic of a self-driving car) can be seen as a combination of basic elements (e.g., pedestrians, trees, and other cars). Due to combinat…
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning Open
Offline RL is a powerful approach for data-driven decision-making and control. Compared to model-free methods, offline model-based RL (MBRL) explicitly learns world models from a static dataset and uses them as surrogate simulators, improv…
ME-IGM: Individual-Global-Max in Maximum Entropy Multi-Agent Reinforcement Learning Open
Multi-agent credit assignment is a fundamental challenge for cooperative multi-agent reinforcement learning (MARL), where a team of agents learn from shared reward signals. The Individual-Global-Max (IGM) condition is a widely used princip…
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization Open
Most reinforcement learning algorithms seek a single optimal strategy that solves a given task. However, it can often be valuable to learn a diverse set of solutions, for instance, to make an agent's interaction with users more engaging, o…
OpenRL: A Unified Reinforcement Learning Framework Open
We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems. OpenRL's robust support for self-play training empowers age…