In this paper we introduce the novel framework of distributionally robust games. These are
multi-player games where each player models the state of nature using a worst-case …

Reinforcement learning (RL) is a subdomain of machine learning concerned with achieving
optimal behavior by interacting with an unknown and potentially stochastic environment. The …

Dynamic Programming and a Neural Network-based value-function approximation
approach have demonstrated superior performance in solving sequential decision making …

Inverted pendulum is one of the classic control problem that could be solved by
reinforcement learning approach. Most of the previous work consider the problem in discrete …

In this paper we introduce the novel framework of distributionally robust games. These are
multi-player games where each player models the state of nature using a worst-case …

Reinforcement learning (RL) is a subdomain of machine learning concerned with achieving
optimal behavior by interacting with an unknown and potentially stochastic environment. The …

Here the Newton's Method direct action selection approach to continuous action-space
reinforcement learning is extended to use an eligibility trace. This is then compared to the …

Cart-pole balancing is a classic control problem that can be solved by reinforcement
learning approach. Most of previous work consider the problem in discrete state space …