Iterative refinement improves continuous control policies learning speed
REFINEPPO: Learning Continuous Control Policies by Iterative Action Refinement
Machine Learning
Summary
Many AI systems that control robots or simulations try to decide the best action in just one step, which can limit how well they perform. The authors suggest letting the AI refine its action several times before finalizing it, improving decisions iteratively. They tested this idea by combining it with a popular learning method and found it often learns faster and sometimes performs better. This approach lets the AI fix its actions step-by-step rather than guessing perfectly at once.
What this means in practice
- •For robotics developers: Improve robot control algorithms by enabling iterative action improvements, speeding up training and potentially enhancing final performance in complex tasks.
- •For autonomous system engineers: Design more efficient control policies for autonomous vehicles or drones that can refine actions multiple times for better decision making under uncertainty.
Authors
Sachini Weerasekara, Sagar Kamarthi, Jacqueline Isaacs
Abstract
Deep reinforcement learning (DRL) has achieved strong performance across a wide range of continuous-control problems. These continuous-control policies, however, are often defined as direct mappings from an observed state to an action or action distribution, requiring a single feed-forward network to construct an optimal control decision in one pass. While effective, this formulation leaves little opportunity for the policy to reconsider or progressively improve an action once an initial prediction has been formed. In this work, we explore an alternative approach: rather than learning only to directly predict an action, can a policy learn to iteratively improve one, and can this iterative process provide advantages during policy learning? We introduce Iterative Action Refinement (IAR), an iterative action-construction method that constructs control actions through a sequence of learned residual corrections. Starting from an initial proposal, a shared refinement network repeatedly conditions on the observed state and the current action proposal, allowing each refinement step to revise the action constructed by preceding steps. The final refined proposal is then used to determine the action executed by the agent. We integrate this iterative action-construction mechanism with Proximal Policy Optimization (PPO), yielding REFINEPPO. We evaluate REFINEPPO across 14 benchmark control tasks, complemented by controlled ablations of refinement depth and update schedules and analyses aimed at understanding why iterative refinement is effective. Across these environments, REFINEPPO matches or exceeds the performance of standard PPO while demonstrating faster convergence on several tasks.