\(\beta\)-dqn: Improving Deep Q-learning By Evolving The Behavior

Abstract

While many sophisticated exploration methods have been proposed, their lack of generality and high computational cost often lead researchers to favor simpler methods like \(\epsilon\)-greedy. Motivated by this, we introduce \(\beta\)-DQN, a simple and efficient exploration method that augments the standard DQN with a behavior function \(\beta\). This function estimates the probability that each action has been taken at each state. By leveraging \(\beta\), we generate a population of diverse policies that balance exploration between state-action coverage and overestimation bias correction. An adaptive meta-controller is designed to select an effective policy for each episode, enabling flexible and explainable exploration. \(\beta\)-DQN is straightforward to implement and adds minimal computational overhead to the standard DQN. Experiments on both simple and challenging exploration domains show that \(\beta\)-DQN outperforms existing baseline methods across a wide range of tasks, providi

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyzhang2025beta

Related papers

• Sampling Efficient Deep Reinforcement Learning Through Preference-guided Stochastic Exploration (2022)8.09
• On The Convergence And Sample Complexity Analysis Of Deep Q-networks With \(\epsilon\)-greedy Exploration (2023)3.58
• DQN With Model-based Exploration: Efficient Learning On Environments With Sparse Rewards (2019)0.00
• The Uncertainty Bellman Equation And Exploration (2017)0.00
• Langevin DQN (2020)0.00
• Learning To Explore With Meta-policy Gradient (2018)0.00
• Careful At Estimation And Bold At Exploration (2023)0.00
• Does DQN Learn? (2022)0.00