Breaking the deadly triad with a target network
The deadly triad refers to the instability of a reinforcement learning algorithm when it employs off-policy learning, function approximation, and bootstrapping simultaneously. In this paper, we investigate the target network as a tool for breaking the deadly triad, providing theoretical support for...
| Hauptverfasser: | , , |
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| Format: | Conference item |
| Sprache: | English |
| Veröffentlicht: |
PMLR
2021
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| _version_ | 1826277639089815552 |
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| author | Zhang, S Yao, H Whiteson, S |
| author_facet | Zhang, S Yao, H Whiteson, S |
| author_sort | Zhang, S |
| collection | OXFORD |
| description | The deadly triad refers to the instability of a reinforcement learning algorithm when it employs off-policy learning, function approximation, and bootstrapping simultaneously. In this paper, we investigate the target network as a tool for breaking the deadly triad, providing theoretical support for the conventional wisdom that a target network stabilizes training. We first propose and analyze a novel target network update rule which augments the commonly used Polyak-averaging style update with two projections. We then apply the target network and ridge regularization in several divergent algorithms and show their convergence to regularized TD fixed points. Those algorithms are off-policy with linear function approximation and bootstrapping, spanning both policy evaluation and control, as well as both discounted and average-reward settings. In particular, we provide the first convergent linear Q-learning algorithms under nonrestrictive and changing behavior policies without bi-level optimization. |
| first_indexed | 2024-03-06T23:31:58Z |
| format | Conference item |
| id | oxford-uuid:6c54909f-a7db-43be-b191-cd97bb8a1fc8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:31:58Z |
| publishDate | 2021 |
| publisher | PMLR |
| record_format | dspace |
| spelling | oxford-uuid:6c54909f-a7db-43be-b191-cd97bb8a1fc82022-03-26T19:10:14ZBreaking the deadly triad with a target networkConference itemhttp://purl.org/coar/resource_type/c_5794uuid:6c54909f-a7db-43be-b191-cd97bb8a1fc8EnglishSymplectic ElementsPMLR2021Zhang, SYao, HWhiteson, SThe deadly triad refers to the instability of a reinforcement learning algorithm when it employs off-policy learning, function approximation, and bootstrapping simultaneously. In this paper, we investigate the target network as a tool for breaking the deadly triad, providing theoretical support for the conventional wisdom that a target network stabilizes training. We first propose and analyze a novel target network update rule which augments the commonly used Polyak-averaging style update with two projections. We then apply the target network and ridge regularization in several divergent algorithms and show their convergence to regularized TD fixed points. Those algorithms are off-policy with linear function approximation and bootstrapping, spanning both policy evaluation and control, as well as both discounted and average-reward settings. In particular, we provide the first convergent linear Q-learning algorithms under nonrestrictive and changing behavior policies without bi-level optimization. |
| spellingShingle | Zhang, S Yao, H Whiteson, S Breaking the deadly triad with a target network |
| title | Breaking the deadly triad with a target network |
| title_full | Breaking the deadly triad with a target network |
| title_fullStr | Breaking the deadly triad with a target network |
| title_full_unstemmed | Breaking the deadly triad with a target network |
| title_short | Breaking the deadly triad with a target network |
| title_sort | breaking the deadly triad with a target network |
| work_keys_str_mv | AT zhangs breakingthedeadlytriadwithatargetnetwork AT yaoh breakingthedeadlytriadwithatargetnetwork AT whitesons breakingthedeadlytriadwithatargetnetwork |