A Practical Guide to Multi-Objective Reinforcement Learning and Planning.
Conor F. HayesRoxana RadulescuEugenio BargiacchiJohan KällströmMatthew MacfarlaneMathieu ReymondTimothy VerstraetenLuisa M. ZintgrafRichard DazeleyFredrik HeintzEnda HowleyAthirai A. IrissappanePatrick MannionAnn NowéGabriel de Oliveira RamosMarcello RestelliPeter VamplewDiederik M. RoijersPublished in: CoRR (2021)
Keyphrases
- multi objective
- reinforcement learning
- multi objective optimization
- evolutionary algorithm
- optimization algorithm
- planning problems
- objective function
- action selection
- particle swarm optimization
- macro actions
- function approximation
- state space
- multiple objectives
- partially observable
- conflicting objectives
- stochastic domains
- deterministic domains
- multi objective optimization problems
- learning algorithm
- pareto optimal
- reward shaping
- multiobjective optimization
- multi objective evolutionary algorithms
- dynamic programming
- nsga ii
- ai planning
- planning systems
- multi agent
- partial observability
- symbolic model checking
- partially observable domains
- neural network
- decision support
- heuristic search
- blocks world
- markov decision processes
- reinforcement learning algorithms
- differential evolution