A Self-Adaptive Reinforcement-Exploration Q-Learning Algorithm

Self-Regulating Action Exploration in Reinforcement Learning

The basic tenet of a learning process is for an agent to learn for only as much and as long as it is necessary. With reinforcement learning, the learning process is divided between exploration and exploitation. Given the complexity of the problem domain and the randomness of the learning process, the exact duration of the reinforcement learning process can never be known with certainty. Using a...

Reinforcement Learning with Exploration

Adaptive Aggregation for Reinforcement Learning with Efficient Exploration: Deterministic Domains

We propose a model-based learning algorithm, the Adaptive Aggregation Algorithm (AAA), that aims to solve the online, continuous state space reinforcement learning problem in a deterministic domain. The proposed algorithm uses an adaptive state aggregation approach, going from coarse to fine grids over the state space, which enables to use finer resolution in the “important” areas of the state ...

Adaptive-Resolution Reinforcement Learning with Efficient Exploration in Deterministic Domains∗

We propose a model-based learning algorithm, the Adaptive-resolution Reinforcement Learning (ARL) algorithm, that aims to solve the online, continuous state space reinforcement learning problem in a deterministic domain. Our goal is to combine adaptive-resolution approximation scheme with efficient exploration in order to obtain fast (polynomial) learning rates. The proposed algorithm uses an a...

RTP-Q: A Reinforcement Learning System with Time Constraints Exploration Planning for Accelerating the Learning Rate

Reinforcement learning is an efficient method for solving Markov Decision Processes that an agent improves its performance by using scalar reward values with higher capability of reactive and adaptive behaviors. Q-learning is a representative reinforcement learning method which is guaranteed to obtain an optimal policy but needs numerous trials to achieve it. k-Certainty Exploration Learning Sy...