Summary of Simulation-based Optimistic Policy Iteration For Multi-agent Mdps with Kullback-leibler Control Cost, by Khaled Nakhleh et al.
Simulation-Based Optimistic Policy Iteration For Multi-Agent MDPs with Kullback-Leibler Control Cost
by Khaled Nakhleh, Ceyhun Eksin, Sabit Ekin
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel agent-based optimistic policy iteration (OPI) scheme is proposed for learning stationary optimal stochastic policies in multi-agent Markov Decision Processes (MDPs), where agents incur a Kullback-Leibler (KL) divergence cost for their control efforts and an additional joint state cost. The OPI scheme consists of a greedy policy improvement step followed by an m-step temporal difference (TD) policy evaluation step, leveraging the separable structure of the instantaneous cost to enable independent computation of improved joint policies. Both synchronous and asynchronous versions of the OPI scheme are shown to converge asymptotically to optimal value functions and policies, with simulation results on a multi-agent MDP validating its performance in minimizing the cost return. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for agents in complex systems to work together effectively. It’s like finding the best route for multiple drivers on a road network. The researchers created a special algorithm that helps each agent make better decisions, taking into account how their actions affect others. They tested this algorithm with different scenarios and found it works well, which is important for building realistic simulations of complex systems. |
