We investigate methods for planning in a Markov Decision Process where the cost function is chosen by an adversary after we fix our policy. As a running example, we consider a rob...
H. Brendan McMahan, Geoffrey J. Gordon, Avrim Blum
Previous works on buffer planning are mainly based on fixed die placement. It is necessary to reduce the complexity of computing the feasible buffer insertion sites to integrate t...
Yuchun Ma, Xianlong Hong, Sheqin Dong, Song Chen, ...
A fundamental task for an autonomous robot is to plan its own motions. Exact approaches to the solution of this motion planning problem suffer from high worst-case running times. ...
Robert-Paul Berretty, Mark H. Overmars, A. Frank v...
Planning in dynamic continuous environments requires reasoning about nonlinear continuous effects, which previous Hierarchical Task Network (HTN) planners do not support. In this ...
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...