123 points by deeplearner123 1 year ago flag hide 12 comments
deeplearning_enthusiast 1 year ago next
This is really interesting! I've been working on optimization problems and this could be a game changer. I'd love to know more about how the deep reinforcement learning (DRL) is being used here.
reinforcement_learning_researcher 1 year ago next
We're using DRL agents that learn policies to make decisions at each step of the optimization process, taking into account the current state of the system and the long-term goals. Our approach is inspired by how AlphaGo and other Alpha-based algorithms learn to make decisions that reduce problem complexity progressively.
machine_learning_engineer 1 year ago prev next
This seems similar to some of the recent research in multi-agent DRL (MADRL), coordination, and consensus. Are you working with multiple agents to solve the optimization problem or relying on a single DRL agent?
reinforcement_learning_researcher 1 year ago next
In this current study, we're only using a single DRL agent. However, we have ongoing research exploring the application of multiple agents, investigating how different coordination mechanisms can improve the optimization performance.
opt_problem_expert 1 year ago prev next
Have you conducted any comparative experiments with traditional optimization techniques? How does your new approach perform concerning computational complexity and quality of solutions?
reinforcement_learning_researcher 1 year ago next
We have conducted experiments comparing to traditional gradient-based methods for specific use-cases. Our new approach generally presents a higher computational cost initially, but as the DRL agent learns from iterative interactions, it becomes more efficient and provides solutions of similarly high quality to traditional techniques.
big_data_analysis 1 year ago prev next
How does your DRL-based approach scale to handle extremely large-scale optimization problems? Are there any challenges with processing high-dimensional data?
reinforcement_learning_researcher 1 year ago next
We're using techniques like function approximation, experience replay, and prioritized experience replay to handle large-scale optimization problems with high-dimensional data. It's still an open research question with ongoing studies regarding scaling DRL methods to much larger problem dimensions and quantities of data.
theoretical_computer_scientist 1 year ago prev next
What formal guarantees of performance can your DRL-based framework provide? Do you have any theoretical analysis of the method?
reinforcement_learning_researcher 1 year ago next
At this stage, our framework doesn't provide formal guarantees on performance. However, we're continuously working on theoretical foundations to support our approach, aiming to better understand convergence properties and error bounds as future work.
new_to_hn 1 year ago prev next
This is mind-blowing! The ability to solve optimization problems that seem impossible through traditional methods might completely disrupt multiple industries, from finance to logistics and manufacturing.
veteran_hn 1 year ago prev next
A worthy addition to the discussion on novel optimization techniques! Welcome to Hacker News, new_to_hn!