9 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Problem Decomposition
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.
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.
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.
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.
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.
Additionally, we show that DaSLaM is not limited by the solver's capabilities as a function of scale; e. g., solver LMs with diverse sizes give significant performance improvement with our solver-agnostic decomposition technique.