DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing

• URL: http://arxiv.org/abs/2311.01450v2
• Date: Sun, 18 Feb 2024 00:59:14 GMT
• Title: DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
• Authors: Vint Lee, Pieter Abbeel, Youngwoon Lee
• Abstract summary: DreamSmooth learns to predict a temporally-smoothed reward, instead of the exact reward at the given timestep. We show that DreamSmooth achieves state-of-the-art performance on long-horizon sparse-reward tasks.
• Score: 60.21269454707625
• License: http://creativecommons.org/licenses/by/4.0/
• 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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