The Calibration Review
RL Algorithms and Methods
Latest research
RL Algorithms and Methods
Distributional RL With C51 and QR-DQN Compared
Two opposite designs for learning return distributions each have different strengths.
- Reward Model Calibration in RLHF PipelinesCalibrating reward models prevents optimization from amplifying measurement error.
- Offline RL Conservative Policies and Out-of-Distribution Action PenalizationConservative policies prevent overconfident decisions by penalizing actions outside training data.
- Ensemble Methods for Epistemic Uncertainty in Deep RLEnsemble disagreement reveals where deep RL agents are ignorant and why it matters.
- Policy Gradient Variance Reduction Techniques for Sparse Reward EnvironmentsA practical tour of baselines, shrinkage, and credit assignment for taming noisy gradients.
RL Algorithms and Methods
View all →- Distributional RL With C51 and QR-DQN ComparedTwo opposite designs for learning return distributions each have different strengths.
- Reward Model Calibration in RLHF PipelinesCalibrating reward models prevents optimization from amplifying measurement error.
- Offline RL Conservative Policies and Out-of-Distribution Action PenalizationConservative policies prevent overconfident decisions by penalizing actions outside training data.
- Ensemble Methods for Epistemic Uncertainty in Deep RLEnsemble disagreement reveals where deep RL agents are ignorant and why it matters.