Proximal Policy Optimization with Adaptive Exploration
On the map: Deep reinforcement learning methods → RL theory and sample complexity
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 4 references that are in the map. First posted on arXiv 7 May 2024.
Builds on
1 of 4 references.
References · 1
- 2017Proximal Policy Optimization Algorithmscited by 15,215
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2024Beyond the Boundaries of Proximal Policy Optimization
- 2024Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy Optimization
- 2025Enhancing PPO with Trajectory-Aware Hybrid Policies
- 2026ExO-PPO: an Extended Off-policy Proximal Policy Optimization Algorithm
- 2025Evolutionary Policy Optimization
- 2020Guided Exploration with Proximal Policy Optimization using a Single Demonstration
- 2025AM-PPO: (Advantage) Alpha-Modulation with Proximal Policy Optimization
- 2026Proximal Policy Optimization with Evolutionary Mutations