Return Trajectory

Return trajectory analysis focuses on understanding and optimizing the sequence of rewards or outcomes over time, a crucial aspect in diverse fields like reinforcement learning and finance. Current research emphasizes improving the efficiency and reliability of predicting and influencing these trajectories, employing techniques like multi-step return averaging, contrastive learning to guide agents towards high-return states, and deep learning models such as LSTMs and GRUs for forecasting. These advancements have implications for enhancing reinforcement learning algorithms, improving investment strategies, and providing a more nuanced understanding of complex systems exhibiting sequential dependencies.

Papers

October 19, 2024

Conformal Predictive Portfolio Selection
Masahiro Kato
Predictive Model Portfolio Selection Conformal Predictor Variance Portfolio Return Trajectory

October 15, 2024

Generalized Distribution Prediction for Asset Returns
Ísak Pétursson, María Óskarsdóttir
Long Short Term Memory Market Return Return Trajectory Distribution Prediction

October 8, 2024

A Case Study of Next Portfolio Prediction for Mutual Funds
Guilherme Thomaz, Denis Maua
Case Study Portfolio Performance Mutual Fund Return Trajectory Asset Allocation

July 2, 2024

ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions
Chan Young Park, Shuyue Stella Li, Hayoung Jung, Svitlana Volkova, Tanushree Mitra, David Jurgens, Yulia Tsvetkov
Policy Value Online Community Social Norm Social Interaction Preference Representation Return Trajectory

February 6, 2024

Averaging $n$-step Returns Reduces Variance in Reinforcement Learning
Brett Daley, Martha White, Marlos C. Machado
Reinforcement Learning Variance Reduction Return Trajectory

February 5, 2024

Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning
Yixiang Shan, Zhengbang Zhu, Ting Long, Qifan Liang, Yi Chang, Weinan Zhang, Liang Yin
Contrastive Learning Offline Reinforcement Learning Task Planning Policy Learning Offline Reinforcement Learning Algorithm Multi View Diffuser Return Trajectory

December 12, 2023

Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
Manon Flageat, Bryan Lim, Antoine Cully
Reinforcement Learning Return Trajectory Policy Reproducibility

October 31, 2023

Beyond Average Return in Markov Decision Processes
Alexandre Marthe, Aurélien Garivier, Claire Vernade
Markov Decision Process Dynamic Programming Distributional Reinforcement Learning Return Trajectory

October 11, 2022

Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning
Tung Nguyen, Qinqing Zheng, Aditya Grover
Offline Reinforcement Learning Behavior Cloning Decision Transformer Return Trajectory

September 28, 2022

A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks
Chau Pham, Trung Dang, Peter Chin
Topological Feature Persistent Homology Random Walk Multi Scale Graph Persistence Diagram Return Trajectory

September 26, 2022

September 18, 2022

Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques
Nghia Chu, Binh Dao, Nga Pham, Huy Nguyen, Hien Tran
Deep Learning Long Short Term Memory System Performance Market Return Mutual Fund Return Trajectory

Return Trajectory

Papers

Conformal Predictive Portfolio Selection

Generalized Distribution Prediction for Asset Returns

A Case Study of Next Portfolio Prediction for Mutual Funds

ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social Interactions

Averaging $n$-step Returns Reduces Variance in Reinforcement Learning

Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning

Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms

Beyond Average Return in Markov Decision Processes

Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning

A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks

Automatic Identification and Classification of Share Buybacks and their Effect on Short-, Mid- and Long-Term Returns

Embedding-based neural network for investment return prediction

Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques