Titlebook: Recent Advances in Reinforcement Learning; 9th European Worksho Scott Sanner,Marcus Hutter Conference proceedings 2012 Springer-Verlag Berl

不安

ETCH

giggle

Incorporate

ℓ1-Penalized Projected Bellman Residualeast-Squares Temporal Difference (LSTD) algorithm with ℓ.-regularization, which has proven to be effective in the supervised learning community. This has been done recently whit the LARS-TD algorithm, which replaces the projection operator of LSTD with an ℓ.-penalized projection and solves the corre

FELON

卡死偷电

上下倒置

Diaphragm

Automatic Construction of Temporally Extended Actions for MDPs Using Bisimulation Metricsucting such actions, expressed as options [24], in a finite Markov Decision Process (MDP). To do this, we compute a bisimulation metric [7] between the states in a small MDP and the states in a large MDP, which we want to solve. The . of this metric is then used to completely define a set of options

东西

Unified Inter and Intra Options Learning Using Policy Gradient Methodsge into AI systems. The options framework, as introduced in Sutton, Precup and Singh (1999), provides a natural way to incorporate macro-actions into reinforcement learning. In the subgoals approach, learning is divided into two phases, first learning each option with a prescribed subgoal, and then

BRAWL

Options with Exceptionsded actions thus allowing us to reuse that solution in solving larger problems. Often, it is hard to find subproblems that are exactly the same. These differences, however small, need to be accounted for in the reused policy. In this paper, the notion of options with exceptions is introduced to addr