Maximizing the Total Reward via Reward Tweaking
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In reinforcement learning, the discount factor γ controls the agent's effective planning horizon. Traditionally, this parameter was considered part of the MDP; however, as deep reinforcement learning algorithms tend to become unstable when the effective planning horizon is long, recent works refer to γ as a hyper-parameter. In this work, we focus on the finite-horizon setting and introduce reward tweaking. Reward tweaking learns a surrogate reward function r̃ for the discounted setting, which induces an optimal (undiscounted) return in the original finite-horizon task. Theoretically, we show that there exists a surrogate reward which leads to optimality in the original task and discuss the robustness of our approach. Additionally, we perform experiments in a high-dimensional continuous control task and show that reward tweaking guides the agent towards better long-horizon returns when it plans for short horizons using the tweaked reward.
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