Dynamic programming, Hamilton-Jacobi reachability, and direct and indirect methods for trajectory optimization. Introduction to model predictive control. Adaptive control, model-based and model-free ...

Next, a safe reinforcement learning framework is proposed by combining model-based policy iteration and state-following-based approximation. Upon real-time data and extrapolated experience data, this ...

16-745: Optimal Control and Reinforcement Learning Spring 2020, TT 4:30-5:50 GHC 4303 Instructor: Chris Atkeson, [email protected] TA: Ramkumar Natarajan [email protected], Office hours Thursdays 6-7 ...

On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference Abstract: We present a reformulation of the stochastic optimal control problem in terms of KL divergence minimisation, ...

When I began to study Reinforcement Learning (RL) in robotics, I noted that there was no literature available that reviewed the varying methods of RL for complex, multistep tasks. To solve this issue, ...

Discover optimal solutions for reinforcement learning in both discrete and continuous action sets. Compare RL solution methods based on value functions. Learn how to solve control tasks with ...

Reinforcement Learning: An Introduction. The MIT Press. Lapan, M. (2018). Deep Reinforcement Learning Hands-On. Birmingham, UK: Packt Publishing. ISBN: 978-1-78883-424-7; Daniel Liberzon CALCULUS OF ...

16-745: Optimal Control and Reinforcement Learning Spring 2019, TT 3-4:20 NSH 3002 Instructor: Chris Atkeson, cga at cmu TA: Preeti Sar, psar1 at andrew, Office hours Tuesday 7 NSH 4508. Events of ...