Search Results for author:
Found 4 papers, 1 papers with code
Assisting humans in complex comparisons: automated information comparison at scale
However, the applications of LLMs for information comparisons face scalability challenges due to the difficulties in maintaining information across large contexts and overcoming model token limitations.
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution
We present Trade Execution using Reinforcement Learning (TradeR) which aims to address two such practical challenges of catastrophy and surprise minimization by formulating trading as a real-world hierarchical RL problem.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Energy-based Surprise Minimization for Multi-Agent Value Factorization
(2) EMIX highlights a practical use of energy functions in MARL with theoretical guarantees and experiment validations of the energy operator.
Implicit Feedback Deep Collaborative Filtering Product Recommendation System
The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations.
