Summary of Adaptive Opponent Policy Detection in Multi-agent Mdps: Real-time Strategy Switch Identification Using Running Error Estimation, by Mohidul Haque Mridul et al.
Adaptive Opponent Policy Detection in Multi-Agent MDPs: Real-Time Strategy Switch Identification Using Running Error Estimation
by Mohidul Haque Mridul, Mohammad Foysal Khan, Redwan Ahmed Rizvee, Md Mosaddek Khan
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 Multi-Agent Reinforcement Learning (MARL) algorithm, OPS-DeMo, is presented for detecting changes in opponents’ policies in dynamic environments. Building upon Proximal Policy Optimization (PPO) and related algorithms like Actor-Critic with Experience Replay (ACER), Trust Region Policy Optimization (TRPO), and Deep Deterministic Policy Gradient (DDPG), OPS-DeMo addresses the limitations of these methods by employing a dynamic error decay to detect policy changes. The algorithm utilizes an Assumed Opponent Policy (AOP) Bank to continuously update beliefs about opponents’ policies, selecting corresponding responses from a pre-trained Response Policy Bank. This approach enables more informed decision-making through precise opponent policy insights and outperforms PPO-trained models in dynamic scenarios like the Predator-Prey setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way for machines to learn together with other machines that are trying to do different things. This is important because it helps machines figure out what the other machines are doing, even if they change their plans quickly. The old ways of teaching machines didn’t work well in these situations, so the new method, called OPS-DeMo, was created. It works by keeping track of what the other machines might do and choosing the best response based on that. This makes it more effective in changing situations like when some machines are trying to catch others. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
