text: Exploration strategy design is one of the challenging problems in reinforcement learning (RL), especially when the environment contains a large state space or sparse rewards. During exploration, the agent tries to discover novel areas or high reward (quality) areas. In most existing methods, the novelty and quality in the neighboring area of the current state are not well utilized to guide the exploration of the agent. To tackle this problem, we propose a novel RL framework, called c lustered r einforcement l earning (CRL), for efficient exploration in RL. CRL adopts clustering to divide the collected states into several clusters, based on which a bonus reward reflecting both novelty and quality in the neighboring area (cluster) of the current state is given to the agent. Experiments on a continuous control task and several games show that CRL can outperform other state-of-the-art methods to achieve the best performance in most cases.
  @cite_6: Several algorithms for learning near-optimal policies in Markov Decision Processes have been analyzed and proven efficient. Empirical results have suggested that Model-based Interval Estimation (MBIE) learns efficiently in practice, effectively balancing exploration and exploitation. This paper presents a theoretical analysis of MBIE and a new variation called MBIE-EB, proving their efficiency even under worst-case conditions. The paper also introduces a new performance metric, average loss, and relates it to its less ''online'' cousins from the literature.
summary: In the tabular setting, there is a finite number of state-action pairs that can directly define a decreasing function of the true visitation count as the exploration bonus. MBIE-EB @cite_6 adds the square root of counts of state-action pairs as the bonus reward to the augmented Bellman equation for exploring less visited ones with theoretical guarantee.