Various disciplines have examined the many phenomena of metacognition and have
produced numerous results, both positive and negative. I discuss some of these aspects of …

What is reinforcement learning? How is reinforcement learning different from stochastic
optimization? And finally, can it be used for applications to quantitative finance for my current …

Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …

From household appliances to applications in robotics, engineered systems involving
complex dynamics can only be as effective as the algorithms that control them. While …

We propose randomized least-squares value iteration (RLSVI)–a new reinforcement
learning algorithm designed to explore and generalize efficiently via linearly parameterized …

The cross-entropy method is an efficient and general optimization algorithm. However, its
applicability in reinforcement learning (RL) seems to be limited because it often converges …

Reinforcement learning, and Q-learning in particular, encounter two major problems when
dealing with large state spaces. First, learning the Q-function in tabular form may be …

Animals' rhythmic movements, such as locomotion, are considered to be controlled by
neural circuits called central pattern generators (CPGs), which generate oscillatory signals …

Approximate reinforcement learning deals with the essential problem of applying
reinforcement learning in large and continuous state-action spaces, by using function …

Reinforcement learning and games have a long and mutually beneficial common history.
From one side, games are rich and challenging domains for testing reinforcement learning …