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.

翻译：基于模型的强化学习（MBRL）因其能够通过生成带有预测奖励的假想轨迹来规划动作，从而以样本高效的方式学习复杂行为而备受关注。尽管取得了成功，但我们惊讶地发现，奖励预测往往是MBRL的瓶颈，尤其是在难以（甚至模糊）预测的稀疏奖励场景中。受人类能够从粗略奖励估计中学习的直觉启发，我们提出了一种简单而有效的奖励平滑方法DreamSmooth，该方法学习预测时间平滑后的奖励，而非给定时间步的精确奖励。我们通过实验表明，DreamSmooth在长视野稀疏奖励任务中在样本效率和最终性能上均达到了最先进水平，同时在常见基准测试（如Deepmind Control Suite和Atari基准测试）中不会损失性能。