DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
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Keywords: Model-based Reinforcement Learning; Reward Shaping; Reward Smoothing
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TL;DR: We show that reward prediction is challenging in many sparse-reward tasks, and propose a simple yet effective method, reward smoothing, which effectively facilitates reward prediction and thus, improves model-based reinforcement learning.
Abstract: Model-based reinforcement learning (MBRL) has gained much attention for its ability to learn complex behaviors in a sample-efficient way: planning actions by generating imaginary trajectories with predicted rewards. Despite its success, we found that surprisingly, reward prediction is often a bottleneck of MBRL, especially for sparse rewards that are challenging (or even ambiguous) to predict. Motivated by the intuition that humans can learn from rough reward estimates, we propose a simple yet effective reward smoothing approach, DreamSmooth, which learns to predict a temporally-smoothed reward, instead of the exact reward at the given timestep. We empirically show that DreamSmooth achieves state-of-the-art performance on long-horizon sparse-reward tasks both in sample efficiency and final performance without losing performance on common benchmarks, such as Deepmind Control Suite and Atari benchmarks.
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Primary Area: reinforcement learning
Submission Number: 1586
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