Problem Decomposition
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
SparseLLM: Towards Global Pruning for Pre-trained Language Models
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands.
Meta-Optimizing Semantic Evolutionary Search
I present MOSES (meta-optimizing semantic evolutionary search), a new probabilistic modeling (estimation of distribution) approach to program evolution.
Decomposition Methods with Deep Corrections for Reinforcement Learning
In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local tasks considering each individual entity independently.
Learning Reward Machines for Partially Observable Reinforcement Learning
RToroIcarte/lrm
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NeurIPS 2019
Reward Machines (RMs), originally proposed for specifying problems in Reinforcement Learning (RL), provide a structured, automata-based representation of a reward function that allows an agent to decompose problems into subproblems that can be efficiently learned using off-policy learning.
Fast reinforcement learning with generalized policy updates
deepmind/deepmind-research
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Proceedings of the National Academy of Sciences 2020
Both strategies considerably reduce the amount of data needed to solve a reinforcement-learning problem.
Decomposition Strategies and Multi-shot ASP Solving for Job-shop Scheduling
We devise and investigate a variety of decomposition strategies in terms of the number and size of time windows as well as heuristics for choosing their operations.
Distilling Reasoning Capabilities into Smaller Language Models
In this work, we propose an alternative reasoning scheme, Socratic CoT, that learns a decomposition of the original problem into a sequence of subproblems and uses it to guide the intermediate reasoning steps.
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation
yimengli46/bellman_point_goal
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In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches with Partially Observable Markov Decision Process (POMDP) formulation of the point goal navigation problem.
Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows
In smaller problem instances, the baseline approach is as competitive as our framework.
Fast Matrix Multiplication Without Tears: A Constraint Programming Approach
In this work, we propose a simple yet novel Constraint Programming approach to find non-commutative algorithms for fast matrix multiplication or provide proof of infeasibility otherwise.
