DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910334444371968 |
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| author | Lee, Vint Abbeel, Pieter Lee, Youngwoon |
| author_facet | Lee, Vint Abbeel, Pieter Lee, Youngwoon |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_01450 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing Lee, Vint Abbeel, Pieter Lee, Youngwoon Machine Learning Artificial Intelligence Robotics 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. |
| title | DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing |
| topic | Machine Learning Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2311.01450 |