Self-Regulating Action Exploration in Reinforcement Learning

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...

Safe Exploration of State and Action Spaces in Reinforcement Learning

In this paper, we consider the important problem of safe exploration in reinforcement learning. While reinforcement learning is well-suited to domains with complex transition dynamics and high-dimensional state-action spaces, an additional challenge is posed by the need for safe and efficient exploration. Traditional exploration techniques are not particularly useful for solving dangerous tasks...

Reinforcement Learning with Exploration

Efficient Exploration in Reinforcement Learning

An agent acting in a world makes observations, takes actions, and receives rewards for the actions taken. Given a history of such interactions, the agent must make the next choice of action so as to maximize the long term sum of rewards. To do this well, an agent may take suboptimal actions which allow it to gather the information necessary to later take optimal or near-optimal actions with res...

Eecient Exploration in Reinforcement Learning

Exploration plays a fundamental role in any active learning system. This study evaluates the role of exploration in active learning and describes several local techniques for exploration in nite, discrete domains, embedded in a reinforcement learning framework (delayed reinforcement). This paper distinguishes between two families of exploration schemes: undirected and directed exploration. Whil...