Research map / Reinforcement learning / Policy gradient and actor-critic
Non-Crossing Quantile Regression for Distributional Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| enforce non-crossing monotone quantile outputs (logit network with cumulative sum and non-negative scale) | QR-DQN with DLTV exploration · Atari-57 | mean human-normalized score: 864% -> 1598% | Helped |
| enforce non-crossing monotone quantile outputs | QR-DQN with DLTV exploration · Atari-57 | median human-normalized score: 193% -> 222% | Helped |
| enforce non-crossing monotone quantile outputs | QR-DQN with DLTV exploration · Atari-57 | games above human: 41 -> 42 | Helped |
| enforce non-crossing quantile outputs | QR-DQN · Atari-57 training time | time to reach same return: about 30% faster | Helped |
Closest papers by meaning
- Distributional Reinforcement Learning With Quantile Regression (AAAI 2018 · similarity 0.92)
- Fully Parameterized Quantile Function for Distributional Reinforcement Learning (NeurIPS 2019 · similarity 0.92)
- Quantile Geometry Regularization for Distributional Reinforcement Learning (NeurIPS 2026 · similarity 0.89)
- Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement Learning (AAAI 2025 · similarity 0.87)
- Single-Trajectory Distributionally Robust Reinforcement Learning (ICML 2024 · similarity 0.85)
- Statistical Efficiency of Distributional Temporal Difference Learning (NeurIPS 2024 · similarity 0.85)
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