Proximal Policy Optimization with Adaptive Exploration
This work addresses a key challenge in reinforcement learning for researchers and practitioners, though it appears incremental as it builds on the widely used PPO algorithm.
The paper tackled the exploration-exploitation tradeoff in reinforcement learning by introducing Proximal Policy Optimization with Adaptive Exploration (axPPO), which dynamically adjusts exploration magnitude during training, resulting in improved learning efficiency over standard PPO algorithms, especially in early stages requiring significant exploration.
Proximal Policy Optimization with Adaptive Exploration (axPPO) is introduced as a novel learning algorithm. This paper investigates the exploration-exploitation tradeoff within the context of reinforcement learning and aims to contribute new insights into reinforcement learning algorithm design. The proposed adaptive exploration framework dynamically adjusts the exploration magnitude during training based on the recent performance of the agent. Our proposed method outperforms standard PPO algorithms in learning efficiency, particularly when significant exploratory behavior is needed at the beginning of the learning process.