Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problem
Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problem explores Leveraging offline reinforcement learning to outperform classical heuristics on complex combinatorial optimization problems like the Traveling Salesman Problem.. Commercial viability score: 7/10 in Combinatorial Optimization.
Updated 2 days ago
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisSelecting Offline Reinforcement Learning Algorithms for Stochastic Network ControlViability: 5.0
- Builds On ThisLearning Optimal and Sample-Efficient Decision Policies with GuaranteesViability: 5.0
- Builds On ThisDecoupling Exploration and Policy Optimization: Uncertainty Guided Tree Search for Hard ExplorationViability: 3.0
- Builds On ThisConstruct, Merge, Solve & Adapt with Reinforcement Learning for the min-max Multiple Traveling Salesman ProblemViability: 3.0
- Builds On ThisTSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents