Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866915841973420032 |
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| author | Malenfant, Dane |
| author_facet | Malenfant, Dane |
| contents | Reusable decision structure survives across episodes in reinforcement learning, but this depends on how the agent--world boundary is drawn. In stationary, finite-horizon MDPs, an invariant core: the (not-necessarily contiguous) subsequences of state--action pairs shared by all successful trajectories (optionally under a simple abstraction) can be constructed. Under mild goal-conditioned assumptions, it's existence can be proven and explained by how the core captures prototypes that transfer across episodes. When the same task is embedded in a decentralized Markov game and the peer agent is folded into the world, each peer-policy update induces a new MDP; the per-episode invariant core can shrink or vanish, even with small changes to the induced world dynamics, sometimes leaving only the individual task core or just nothing. This policy-induced non-stationarity can be quantified with a variation budget over the induced kernels and rewards, linking boundary drift to loss of invariants. The view that a continual RL problem arises from instability of the agent--world boundary (rather than exogenous task switches) in decentralized MARL suggests future work on preserving, predicting, or otherwise managing boundary drift. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06813 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary Malenfant, Dane Artificial Intelligence Reusable decision structure survives across episodes in reinforcement learning, but this depends on how the agent--world boundary is drawn. In stationary, finite-horizon MDPs, an invariant core: the (not-necessarily contiguous) subsequences of state--action pairs shared by all successful trajectories (optionally under a simple abstraction) can be constructed. Under mild goal-conditioned assumptions, it's existence can be proven and explained by how the core captures prototypes that transfer across episodes. When the same task is embedded in a decentralized Markov game and the peer agent is folded into the world, each peer-policy update induces a new MDP; the per-episode invariant core can shrink or vanish, even with small changes to the induced world dynamics, sometimes leaving only the individual task core or just nothing. This policy-induced non-stationarity can be quantified with a variation budget over the induced kernels and rewards, linking boundary drift to loss of invariants. The view that a continual RL problem arises from instability of the agent--world boundary (rather than exogenous task switches) in decentralized MARL suggests future work on preserving, predicting, or otherwise managing boundary drift. |
| title | Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.06813 |