Simone Parisi
Papers
3
Total Citations
67
H-Index
2
About
Simone Parisi is a leading researcher in multi-objective reinforcement learning (MORL), a field that addresses real-world control problems—from robotics to economics—where agents must balance multiple conflicting goals. His seminal work, *"Multi-objective Reinforcement Learning through Continuous Pareto Manifold Approximation"* (2016, 51 citations), introduced a novel method for approximating the continuous Pareto frontier, enabling agents to learn a spectrum of optimal trade-offs rather than a single solution. This contribution fundamentally advanced how autonomous systems handle complex, multi-criteria decision-making. Parisi further demonstrated the practical power of RL in *"Reinforcement learning vs human programming in tetherball robot games"* (2015, 14 citations), where he showed that RL can match or exceed human-coded strategies in dynamic motor skill tasks, highlighting RL's potential for robotic autonomy. His later work on *"Local-utopia policy selection for MORL"* (2016) refined how agents select among Pareto-optimal policies, improving efficiency in multi-objective settings. With over 67 combined citations, Parisi’s research bridges theoretical MORL advances with tangible robotic applications, establishing him as a key figure in creating learning systems that can autonomously navigate real-world complexity.
Research Focus
Key Achievements
Top Papers
- 1
- 2Reinforcement learning vs human programming in tetherball robot games14 citations · 2015
- 3Local-utopia policy selection for multi-objective reinforcement learning2 citations · 2016