Methods › Reinforcement Learning › Off-Policy TD Control › Expected Sarsa
Expected Sarsa
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Expected Sarsa is like Q-learning but instead of taking the maximum over next state-action pairs, we use the expected value, taking into account how likely each action is under the current policy.
Q(Sₜ, Aₜ) ←Q(Sₜ, Aₜ) + α[Rₜ₊₁ + γ∑ₐπ(a|Sₜ₊₁)Q(Sₜ₊₁, a) - Q(Sₜ, Aₜ)]
Except for this change to the update rule, the algorithm otherwise follows the scheme of Q-learning. It is more computationally expensive than Sarsa but it eliminates the variance due to the random selection of Aₜ₊₁.
Source: Sutton and Barto, Reinforcement Learning, 2nd Edition
Papers archive 2025-07-28
9 shown of 9, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Reinforcement Learning for Hanabi 31 May 2025 · 0 repositories · arXiv:2506.00458
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Convergent NMPC-based Reinforcement Learning Using Deep Expected Sarsa and Nonlinear Temporal Difference Learning 7 Feb 2025 · 0 repositories · arXiv:2502.04925
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Solving Royal Game of Ur Using Reinforcement Learning 23 Aug 2022 · 2 repositories · arXiv:2208.10669
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On the Convergence of SARSA with Linear Function Approximation 14 Feb 2022 · 0 repositories · arXiv:2202.06828
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A study of first-passage time minimization via Q-learning in heated gridworlds 5 Oct 2021 · 0 repositories · arXiv:2110.02129
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Chrome Dino Run using Reinforcement Learning 15 Aug 2020 · 0 repositories · arXiv:2008.06799
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Model-free Reinforcement Learning for Stochastic Stackelberg Security Games 24 May 2020 · 0 repositories · arXiv:2005.11853
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The Concept of Criticality in Reinforcement Learning 16 Oct 2018 · 0 repositories · arXiv:1810.07254
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Multi-step Reinforcement Learning: A Unifying Algorithm 3 Mar 2017 · 0 repositories · arXiv:1703.01327
Tasks archive 2025-07-28
7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Reinforcement Learning (RL) | 8 |
| reinforcement-learning | 7 |
| Reinforcement Learning | 6 |
| Q-Learning | 4 |
| Board Games | 1 |
| Card Games | 1 |
| Deep Reinforcement Learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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