In recent years, the research on reinforcement learning (RL) has focused on function
approximation in learning prediction and control of Markov decision processes (MDPs). The …

Reinforcement learning is a learning paradigm concerned with learning to control a system
so as to maximize a numerical performance measure that expresses a long-term objective …

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

Policy evaluation is an essential step in most reinforcement learning approaches. It yields a
value function, the quality assessment of states for a given policy, which can be used in a …

Policy evaluation is concerned with estimating the value function that predicts long-term
values of states under a given policy. It is a crucial step in many reinforcement-learning …

Many traditional reinforcement-learning algorithms have been designed for problems with
small finite state and action spaces. Learning in such discrete problems can been difficult …

We address the question of how the approximation error/Bellman residual at each iteration
of the Approximate Policy/Value Iteration algorithms influences the quality of the resulted …

Reinforcement learning has been successful across several applications in which agents
have to learn to act in environments with sparse feedback. However, despite this empirical …

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

The family of temporal difference (TD) methods span a spectrum from computationally frugal
linear methods like TD (λ) to data efficient least squares methods. Least square methods …