adaptive-control

This paper introduces ACSAC, a reinforcement learning method that uses an adaptive chunk size actor-critic algorithm with a causal Transformer Q-network to handle long-horizon, sparse-reward tasks. It demonstrates state-of-the-art performance on manipulation tasks by dynamically adjusting action chunk sizes based on state-dependent needs.

This paper proposes a meta-control architecture using temporal self-attention for adaptive control of Euler-Lagrange systems with unobservable memory states. It demonstrates improved tracking performance over baseline methods on a 2-DOF manipulator while identifying failure modes in long-memory regimes.