An Agency-Transferring Model-Free Policy Enhancement Technique

Researchers propose a reinforcement learning technique that accelerates policy training by gradually transferring control from a baseline policy to a learnable policy, achieving faster convergence and superior performance compared to training from scratch while maintaining high success rates throughout the learning process.

This research addresses a fundamental efficiency problem in reinforcement learning: the computational expense and complexity of training policies from scratch. The proposed agency-transfer method bridges baseline policies and learned policies through a dynamic arbitration mechanism, allowing systems to leverage existing functional solutions while improving upon them. This approach has significant implications for practical AI deployment, where perfect from-scratch training is often economically infeasible.

The method's theoretical foundation formalizes what constitutes a functional baseline policy—one that reliably reaches goals and maintains stable states. By exploiting this property during training, the system maintains consistently high goal-reaching rates from inception, a critical advantage over standard RL approaches that often struggle during early training phases. The researchers provide formal lower bounds on success probabilities for the final baseline-free policy, offering theoretical guarantees that extend beyond empirical observation.

For practitioners developing control systems, this technique reduces computational requirements while delivering stronger final performance than competitive approaches. The methodology particularly benefits domains where suboptimal baseline policies already exist—a common scenario in robotics, industrial control, and autonomous systems. The standalone neural network produced at training completion eliminates runtime dependency on baseline systems, enabling clean deployment without additional inference overhead.

The empirical validation on continuous-control benchmarks demonstrates the approach matches or exceeds state-of-the-art methods while maintaining superior safety metrics throughout training. This combination—faster convergence, better final performance, and higher reliability—positions this technique as a valuable addition to the RL toolkit for practitioners balancing development speed with safety constraints.

• →Agency-transfer method accelerates RL training by gradually shifting control from baseline to learning policy
• →Maintains high goal-reaching rates throughout training, addressing a critical weakness of standard RL approaches
• →Final learned policy operates independently without baseline support while exceeding performance of competitive methods
• →Theoretical framework provides formal guarantees on success probabilities for the baseline-free regime
• →Reduces computational training costs while producing superior policies compared to from-scratch learning