Dynamic Context Scheduling Boosts RL Generalization and Performance

This research investigates Dynamic Context Scheduling as an innovative training mechanism for contextual reinforcement learning (RL). Unlike traditional methods that treat context variation as a deployment reality, this approach actively controls and shapes context evolution within each training episode. By exposing the policy to a more diverse and temporally structured range of environment parameters, the system learns to adapt more effectively. The DYNAMICCARLENV framework was introduced to wrap contextual environments with pluggable schedule families, such as sinusoidal offsets or cosine annealing. Experiments across various environments like CartPole, BipedalWalker, and VehicleRacing demonstrated that dynamic schedules either matched or surpassed static context baselines in out-of-distribution (OOD) scenarios. Notably, for more complex environments, dynamic scheduling also led to higher in-distribution (ID) evaluation performance, suggesting improved generalization and overall robustness.