Learnings Options End-to-End for Continuous Action Tasks

The Sciences of the Artificial

Herbert A. Simon · 1969

Earlier work this paper cites.

Simple statistical gradient-following algorithms for connectionist reinforcement learning

Ronald J. Williams · 1992

Earlier work this paper cites.

Policy gradient methods for reinforcement learning with function approximation

Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour · 1999

Earlier work this paper cites.

Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning

Richard S. Sutton, Doina Precup, and Satinder P. Singh · 1999

Earlier work this paper cites.

Temporal abstraction in reinforcement learning

Doina Precup · 2000

Earlier work this paper cites.

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