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An Open-Ended Learning Framework for Opponent Modeling
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Proposes OEOM, an open-ended framework that generates diverse-strength and diverse-style opponents via population-based training plus trajectory-space diversity regularization, and IOM, an in-context RL Transformer opponent model, showing better generalization to unseen opponents in PP, LBF and Overcooked.
Problem
Opponent models trained against a fixed, pre-designated set of training opponents generalize poorly to unseen test opponents whose behaviors are out-of-distribution.
Approach
Continuously generate a growing set of opponents that differ in strength (via population-based training with cross-play and natural selection) and in style (via an information-theoretic trajectory space diversity regularizer), and feed this set to any opponent modeling approach; the proposed IOM uses a Transformer conditioned on opponent trajectories trained in an in-context RL/Seq2Seq fashion.
Key results
- OEOM improves generalization of OM approaches over Script, Self-Play and ablations, especially on unseen opponents in PP and OC, with most OM approaches achieving best Unseen-Avg results under OEOM.
- The proposed IOM generally outperforms DRON, LIAM, MeLIBA and BC across settings, notably in Unseen-L3 of PP when trained with OEOM.
- Performance of OM approaches generally improves as the size of the OEOM-generated training set grows (6 to 42 policies).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| train with OEOM-generated opponents instead of Script, Self-Play, or OEOM ablations | OM approaches · Predator Prey, Unseen-Avg. | Avg. Ret. | Helped |
| train with OEOM-generated opponents instead of Script, Self-Play, or OEOM ablations | OM approaches · OverCooked, Unseen-Avg. | Avg. Ret. | Helped |
| train with OEOM-generated opponents instead of Script, Self-Play, or OEOM ablations | OM approaches · Level-Based Foraging, Unseen-Avg. | Avg. Ret. | Helped |
| use IOM instead of DRON, LIAM, MeLIBA, or BC | OEOM-trained opponent modeling · Predator Prey | Avg. Ret. | Helped |
| use IOM instead of DRON, LIAM, MeLIBA, or BC | OEOM-trained opponent modeling · Level-Based Foraging | Avg. Ret. | Helped |
| use IOM instead of DRON, LIAM, MeLIBA, or BC | OEOM-trained opponent modeling · OverCooked | Avg. Ret. | Helped |
| add TSD regularization to PBT (ablation) | OEOM · Predator Prey, Unseen-Avg. | Avg. Ret. | Helped |
| add the PBT RL objective to TSD optimization (ablation) | OEOM · Predator Prey, Unseen-Avg. | Avg. Ret. | Helped |
| add TSD regularization to PBT (ablation) | OEOM · Level-Based Foraging, Unseen-Avg. | Avg. Ret. | Helped |
| add the PBT RL objective to TSD optimization (ablation) | OEOM · Level-Based Foraging, Unseen-Avg. | Avg. Ret. | Helped |
| add TSD regularization to PBT (ablation) | OEOM · OverCooked, Unseen-Avg. | Avg. Ret. | Helped |
| add the PBT RL objective to TSD optimization (ablation) | OEOM · OverCooked, Unseen-Avg. | Avg. Ret. | Helped |
Surprising findings
- Other OM baselines often perform worse than behavioral cloning when facing unseen opponents at test time, even though they beat BC during training.
- In PP, some baselines perform worse as the training set grows, while IOM scales steadily.
Limitations
- Opponents in the generated set are fixed policies, so OM approaches may struggle against opponents that continue to learn or adapt at test time.
- Evaluation limited to three sparse-reward environments.
Experimental setup
Datasets: Predator Prey Level-Based Foraging OverCooked
Benchmarks: Predator Prey Level-Based Foraging OverCooked
Metrics: average return
Baselines: DRON LIAM MeLIBA Behavioral Cloning Script Self-Play
Builds on: Population-Based Training PPO DRON LIAM MeLIBA
Closest papers by meaning
- Opponent Modeling with In-context Search (NeurIPS 2024 · similarity 0.92)
- Opponent Modeling based on Subgoal Inference (NeurIPS 2024 · similarity 0.91)
- Offline Opponent Modeling with Truncated Q-driven Instant Policy Refinement (ICML 2025 · similarity 0.89)
- Generalized Intention Modeling in Multi-Agent Reinforcement Learning (NeurIPS 2026 · similarity 0.88)
- Opponent Shaping in LLM Agents (ICLR 2026 · similarity 0.87)
- Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning (ICML 2024 · similarity 0.86)
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