Research map / Reinforcement learning / Visual and representation RL
Information Design in Multi-Agent Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add obedience constraints to policy-gradient learning (ablation) | PGOC versus PG · Recommendation Letter | sender reward | Helped |
| add signaling gradient to policy-gradient learning (ablation) | SG versus PG · Recommendation Letter | sender reward | No clear effect |
| add obedience constraints to signaling-gradient learning (ablation) | SGOC versus SG · Recommendation Letter | sender reward | Helped |
| use DIAL instead of the proposed method | Recommendation Letter · Recommendation Letter | receiver reward | Helped |
| use PG or SG instead of an algorithm with obedience constraints (ablation) | Recommendation Letter · Recommendation Letter | sender reward | Hurt |
| add signaling gradient to communication learning (ablation) | SGOC · Reaching Goals | sender reward | Helped |
| increase the honesty constraint parameter ε (ablation) | SGOC · Recommendation Letter | honesty | Helped |
| increase the Lagrangian parameter λ (ablation) | SGOC · Recommendation Letter | honesty | Helped |
| use the Lagrangian method instead of dual gradient descent (ablation) | SGOC · Recommendation Letter and Reaching Goals | performance | Helped |
Closest papers by meaning
- Learning to Incentivize Other Learning Agents (NeurIPS 2020 · similarity 0.87)
- Learning to Share and Hide Intentions using Information Regularization (NeurIPS 2018 · similarity 0.86)
- Online Information Acquisition: Hiring Multiple Agents (ICLR 2024 · similarity 0.85)
- Steering the Herd: A Framework for LLM-based Control of Social Learning (ICLR 2026 · similarity 0.85)
- On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games (NeurIPS 2024 · similarity 0.85)
- On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents (ICML 2026 · similarity 0.85)
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