Poster
Taylor Expansion Policy Optimization
Yunhao Tang · Michal Valko · Remi Munos
Keywords: [ Reinforcement Learning - Deep RL ] [ Reinforcement Learning ] [ Deep Reinforcement Learning ]
In this work, we investigate the application of Taylor expansions in reinforcement learning. In particular, we propose Taylor Expansion Policy Optimization, a policy optimization formalism that generalizes prior work as a first-order special case. We also show that Taylor expansions intimately relate to off-policy evaluation. Finally, we show that this new formulation entails modifications which improve the performance of several state-of-the-art distributed algorithms.