Reinforcement Learning Conference

The Reinforcement Learning Conference (RLC 2025) is a dedicated international venue for research on reinforcement learning, the paradigm in which agents learn optimal behavior through interaction with an environment via trial, error, and reward signals. Topics span theoretical foundations including regret bounds and sample complexity, deep RL algorithms and architectures, multi-agent RL, offline and model-based RL, safe and constrained RL, and applications in robotics, game playing, healthcare, and autonomous systems.

RLC provides a focused community for reinforcement learning researchers who want a venue entirely centered on their sub-field, rather than competing for attention within broader AI or ML conferences. It attracts theoreticians studying the mathematics of RL, practitioners deploying RL in real systems, and cognitive scientists interested in RL as a model of biological learning, making it a productive forum for the full spectrum of researchers who see reward-based learning as a central paradigm for understanding and building intelligent systems.