Research map / Learning theory and algorithms / Sample complexity
Ensemble Bootstrapping for Q-Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI Asterix | score: 5718.1 -> 22152.5 | Helped |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI Breakout | score: 333.4 -> 406.3 | Helped |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI CrazyClimber | score: 111765.4 -> 127967.5 | Helped |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI DoubleDunk | score: -15.8 -> -10.1 | Helped |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI Gopher | score: 6940.5 -> 21940.0 | Helped |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI Pong | score: 19.9 -> 21.0 | Helped |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI PrivateEye | score: 100.0 -> 100.0 | No clear effect |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI Qbert | score: 6277.1 -> 14384.4 | Helped |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI RoadRunner | score: 40264.9 -> 55927.5 | Helped |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI Tennis | score: -1.2 -> -1.1 | Helped |
| Use an ensemble of Q estimators and average the non-selecting estimators for the bootstrap value | Deep Q-learning · ATARI VideoPinball | score: 230534.3 -> 361205.1 | Helped |
| Increase the ensemble size (ablation) | EBQL · Meta Chain MDP | performance | Helped |
Closest papers by meaning
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- Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities (NeurIPS 2026 · similarity 0.85)
- Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning (ICLR 2025 · similarity 0.85)
- A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning (AAAI 2024 · similarity 0.85)
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