Research map / Multi-agent systems and games / Cooperative multi-agent RL
Learning to Act in Decentralized Partially Observable MDPs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learning with oSARSA (occupancy-state SARSA over plans) instead of REINFORCE | Boxpushing · Boxpushing h=10 | V_rho(s0) | Mixed |
| Using MILP-based greedy maximization with oSARSA | oSARSA · Tiger, Boxpushing, Grid3x3corners | V_rho(s0) | No clear effect |
| Restricting to plans (linear value function) with oSARSA | Tiger h=7 · Tiger h=7 | V_rho(s0) | Hurt |
Closest papers by meaning
- Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments (AAAI 2016 · similarity 0.92)
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- Optimally Solving Simultaneous-Move Dec-POMDPs: The Sequential Central Planning Approach (AAAI 2025 · similarity 0.89)
- Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning (ICML 2024 · similarity 0.89)
- Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning (NeurIPS 2025 · similarity 0.89)
- Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies (ICML 2024 · similarity 0.88)
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