SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking
The episode discusses SCoCaT, a method for success-conditioned constrained reinforcement learning for spacecraft docking. Hosts discuss how SCoCaT fixes 'feasibility collapse' by introducing a per-step success indicator, Isucc,t. They conclude that this structural fix allows agents to achieve both high task engagement and safety compliance in terminal navigation tasks while maintaining simplicity for deployment.