DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
ICLR 2024(2023)
摘要
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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关键词
Model-based Reinforcement Learning,Reward Shaping,Reward Smoothing
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