Taylor Expansion Policy Optimization

Yunhao Tang · Michal Valko · Remi Munos

Keywords: [ Reinforcement Learning ] [ Deep Reinforcement Learning ] [ Reinforcement Learning - Deep RL ]

[ Abstract ]
Tue 14 Jul 7 a.m. PDT — 7:45 a.m. PDT
Tue 14 Jul 6 p.m. PDT — 6:45 p.m. PDT


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.

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