Problem Decomposition

3 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Meta-Optimizing Semantic Evolutionary Search

opencog/moses Association for Computing Machinery (ACM) 2017

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

sisl/AutomotivePOMDPs.jl 6 Feb 2018

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 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.