Proximal Policy Optimization Smoothed Algorithm (2012.02439v1)

Abstract: Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrogate objective function to restrict the step size at each policy update. Although such restriction is helpful, the algorithm still suffers from performance instability and optimization inefficiency from the sudden flattening of the curve. To address this issue we present a PPO variant, named Proximal Policy Optimization Smooth Algorithm (PPOS), and its critical improvement is the use of a functional clipping method instead of a flat clipping method. We compare our method with PPO and PPORB, which adopts a rollback clipping method, and prove that our method can conduct more accurate updates at each time step than other PPO methods. Moreover, we show that it outperforms the latest PPO variants on both performance and stability in challenging continuous control tasks.

• The paper introduces a novel functional clipping method that replaces flat clipping to allow smoother and more accurate policy updates.
• The paper compares PPOS with both PPO and PPO-Rollback, demonstrating enhanced update accuracy and learning efficiency.
• The paper provides empirical evidence that PPOS improves stability and performance in challenging continuous control tasks.

The paper "Proximal Policy Optimization Smoothed Algorithm" presents a novel variant of the Proximal Policy Optimization (PPO) algorithm, termed the Proximal Policy Optimization Smooth Algorithm (PPOS). The primary innovation of this work is the introduction of a functional clipping method to replace the traditional flat clipping method used in PPO.

Key Contributions and Innovations:

1. Functional Clipping Method:
   • The traditional PPO algorithm uses a flat clipping method to restrict the step size during policy updates. While this helps in maintaining stability, it can lead to performance instability and inefficiency from the sudden flattening of the learning curve.
   • The proposed PPOS algorithm introduces a functional clipping method. This method allows for smoother and potentially more accurate updates at each timestep by avoiding the abrupt transitions caused by flat clipping.
2. Comparative Analysis:
   • The authors compare PPOS with the original PPO algorithm and an alternative variant, PPO-Rollback (PPORB), which utilizes a rollback clipping method.
   • The comparative results demonstrate that PPOS performs better in terms of both update accuracy and overall learning efficiency.
3. Performance Evaluation:
   • The paper provides an empirical evaluation of PPOS on several challenging continuous control tasks.
   • The results indicate that PPOS not only achieves higher performance metrics but also exhibits improved stability compared to the latest PPO variants.

Implications:

The introduction of the functional clipping method addresses a significant drawback in the traditional PPO algorithm by enabling more controlled and smooth updates. This advancement has the potential to enhance the performance and stability of reinforcement learning models, particularly in complex control tasks. The findings suggest that adopting such a method could lead to more efficient optimization processes and ultimately better model performance in practical applications.