Reinforcement learning defines the problem facing agents that learn to make good decisions through action and observation alone. To be effective problem solvers, such agents …
This is an updated version of original (DOI: 10.26300/1pad-7574) that contains minor revisions (see page iv for list of revisions). Reinforcement learning defines the problem …
Reusing knowledge allows intelligent systems to learn solutions to complex tasks more quicker by avoiding re-learning the components of the solution from scratch. Recent advances …
Reinforcement learning (RL) techniques have led to remarkable results in challenging domains such as Atari games, Go, and Starcraft, suggesting that practical applications lie just …
In this work, I present an implemented model that can learn interactively from natural language, enabling non-expert human trainers to convey complex tasks to machines …
In both supervised and reinforcement settings, there exist learning problems that are hard due to having high computational or sample complexity. Researchers have shown, using …
Educational assessments are crucial for both instructors and education researchers to measure learning, troubleshoot student problems, evaluate pedagogy, and improve education. Unfortunately, creating and administering …
Reinforcement learning (RL) is the study of the interaction between an environment and an artificial agent that learns to maximize reward through trial-and-error. Owing to …