This paper introduces a framework for understanding world models in reinforcement learning by categorizing them based on the channel they model: the environment, the agent, or the joint agent-environment system. Utilizing computational mechanics, the authors define canonical predictive models for these three cases as epsilon-transducers or epsilon-machines. The research demonstrates that canonical environment models align with standard predictive state representations, while the other two cases yield analogous canonical models for the agent and the joint system. A key finding is that canonical support-restricted environment states are factored through canonical joint causal states, with their transition structure derived from the joint model, and the agent-side construction is dual. AI
IMPACT Provides a theoretical foundation for developing more sophisticated and efficient AI agents by clarifying the structure and purpose of world models.
RANK_REASON Academic paper published on arXiv detailing a new theoretical framework for world models in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- computational mechanics
- epsilon-machines
- epsilon-transducers
- partially observable Markov decision process
- Predictive State Representations
- World Models
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