Empirical Design in Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Patterson, Andrew, Neumann, Samuel, White, Martha, White, Adam |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
by: Patterson, Andrew, et al.
Published: (2021)
by: Patterson, Andrew, et al.
Published: (2021)
Deep Reinforcement Learning with Gradient Eligibility Traces
by: Elelimy, Esraa, et al.
Published: (2025)
by: Elelimy, Esraa, et al.
Published: (2025)
When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
by: Liu, Vincent, et al.
Published: (2023)
by: Liu, Vincent, et al.
Published: (2023)
Investigating the Interplay of Prioritized Replay and Generalization
by: Panahi, Parham Mohammad, et al.
Published: (2024)
by: Panahi, Parham Mohammad, et al.
Published: (2024)
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning
by: Patterson, Andrew, et al.
Published: (2024)
by: Patterson, Andrew, et al.
Published: (2024)
Revisiting Mixture Policies in Entropy-Regularized Actor-Critic
by: He, Jiamin, et al.
Published: (2026)
by: He, Jiamin, et al.
Published: (2026)
Real-Time Recurrent Learning using Trace Units in Reinforcement Learning
by: Elelimy, Esraa, et al.
Published: (2024)
by: Elelimy, Esraa, et al.
Published: (2024)
A New View on Planning in Online Reinforcement Learning
by: Roice, Kevin, et al.
Published: (2024)
by: Roice, Kevin, et al.
Published: (2024)
Fine-Tuning without Performance Degradation
by: Wang, Han, et al.
Published: (2025)
by: Wang, Han, et al.
Published: (2025)
Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning
by: Wahab, Abdul, et al.
Published: (2026)
by: Wahab, Abdul, et al.
Published: (2026)
A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
by: Adkins, Jacob, et al.
Published: (2024)
by: Adkins, Jacob, et al.
Published: (2024)
Rethinking the Foundations for Continual Reinforcement Learning
by: Elelimy, Esraa, et al.
Published: (2025)
by: Elelimy, Esraa, et al.
Published: (2025)
Harnessing Discrete Representations For Continual Reinforcement Learning
by: Meyer, Edan, et al.
Published: (2023)
by: Meyer, Edan, et al.
Published: (2023)
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
by: Zhu, Lingwei, et al.
Published: (2023)
by: Zhu, Lingwei, et al.
Published: (2023)
Forager: a lightweight testbed for continual learning with partial observability in RL
by: Tang, Steven, et al.
Published: (2026)
by: Tang, Steven, et al.
Published: (2026)
AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning
by: Pramanik, Subhojeet, et al.
Published: (2023)
by: Pramanik, Subhojeet, et al.
Published: (2023)
Gradient Iterated Temporal-Difference Learning
by: Vincent, Théo, et al.
Published: (2026)
by: Vincent, Théo, et al.
Published: (2026)
Demystifying the Recency Heuristic in Temporal-Difference Learning
by: Daley, Brett, et al.
Published: (2024)
by: Daley, Brett, et al.
Published: (2024)
Goal-Space Planning with Subgoal Models
by: Lo, Chunlok, et al.
Published: (2022)
by: Lo, Chunlok, et al.
Published: (2022)
An Analysis of Action-Value Temporal-Difference Methods That Learn State Values
by: Daley, Brett, et al.
Published: (2025)
by: Daley, Brett, et al.
Published: (2025)
Human-Inspired Multi-Level Reinforcement Learning
by: Wu, Mingkang, et al.
Published: (2025)
by: Wu, Mingkang, et al.
Published: (2025)
Deep Double Q-learning
by: Nagarajan, Prabhat, et al.
Published: (2025)
by: Nagarajan, Prabhat, et al.
Published: (2025)
Distributions as Actions: A Unified Framework for Diverse Action Spaces
by: He, Jiamin, et al.
Published: (2025)
by: He, Jiamin, et al.
Published: (2025)
Performance Optimization of Ratings-Based Reinforcement Learning
by: Rose, Evelyn, et al.
Published: (2025)
by: Rose, Evelyn, et al.
Published: (2025)
Rating-based Reinforcement Learning
by: White, Devin, et al.
Published: (2023)
by: White, Devin, et al.
Published: (2023)
The Cell Must Go On: Agar.io for Continual Reinforcement Learning
by: Mohamed, Mohamed A., et al.
Published: (2025)
by: Mohamed, Mohamed A., et al.
Published: (2025)
Symmetric Behavior Regularized Policy Optimization
by: Zhu, Lingwei, et al.
Published: (2025)
by: Zhu, Lingwei, et al.
Published: (2025)
Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
by: Schlegel, Matthew, et al.
Published: (2026)
by: Schlegel, Matthew, et al.
Published: (2026)
Investigating the Histogram Loss in Regression
by: Imani, Ehsan, et al.
Published: (2024)
by: Imani, Ehsan, et al.
Published: (2024)
Mitigating Value Hallucination in Dyna Planning via Multistep Predecessor Models
by: Aminmansour, Farzane, et al.
Published: (2020)
by: Aminmansour, Farzane, et al.
Published: (2020)
The Formalism-Implementation Gap in Reinforcement Learning Research
by: Castro, Pablo Samuel
Published: (2025)
by: Castro, Pablo Samuel
Published: (2025)
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design
by: Garcin, Samuel, et al.
Published: (2024)
by: Garcin, Samuel, et al.
Published: (2024)
Component Based Quantum Machine Learning Explainability
by: White, Barra, et al.
Published: (2025)
by: White, Barra, et al.
Published: (2025)
A Survey of State Representation Learning for Deep Reinforcement Learning
by: Echchahed, Ayoub, et al.
Published: (2025)
by: Echchahed, Ayoub, et al.
Published: (2025)
Auxiliary task discovery through generate-and-test
by: Rafiee, Banafsheh, et al.
Published: (2022)
by: Rafiee, Banafsheh, et al.
Published: (2022)
VISTA: A Panoramic View of Neural Representations
by: White, Tom
Published: (2024)
by: White, Tom
Published: (2024)
Environment Design for Inverse Reinforcement Learning
by: Buening, Thomas Kleine, et al.
Published: (2022)
by: Buening, Thomas Kleine, et al.
Published: (2022)
Exploration in Knowledge Transfer Utilizing Reinforcement Learning
by: Jedlička, Adam, et al.
Published: (2024)
by: Jedlička, Adam, et al.
Published: (2024)
Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
by: Jajoo, Pranaya, et al.
Published: (2026)
by: Jajoo, Pranaya, et al.
Published: (2026)
Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers
by: Barron, Joshua, et al.
Published: (2025)
by: Barron, Joshua, et al.
Published: (2025)
Similar Items
-
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
by: Patterson, Andrew, et al.
Published: (2021) -
Deep Reinforcement Learning with Gradient Eligibility Traces
by: Elelimy, Esraa, et al.
Published: (2025) -
When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
by: Liu, Vincent, et al.
Published: (2023) -
Investigating the Interplay of Prioritized Replay and Generalization
by: Panahi, Parham Mohammad, et al.
Published: (2024) -
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning
by: Patterson, Andrew, et al.
Published: (2024)