Researchers introduce the Terminal Representation (TR), a novel approach to representation learning in reinforcement learning that encodes reward-weighted trajectories more efficiently than existing methods. The TR achieves comparable performance to established approaches like the Default Representation while reducing computational overhead and eliminating assumptions about symmetric transition dynamics.
The Terminal Representation represents an incremental but meaningful advancement in how machine learning systems abstract and learn from sequential decision-making tasks. Rather than requiring computationally expensive eigendecomposition operations, the TR can be learned as a lower-dimensional object while capturing equivalent knowledge. This efficiency gain matters because representation learning serves as a foundation for downstream applications including option discovery, transfer learning, and exploration strategies—capabilities critical to making reinforcement learning systems more generalizable and sample-efficient.
The theoretical contribution builds on decades of work in value-based and trajectory-based representations. The Successor Representation and Default Representation established that encoding states through their future consequences—weighted by reward—provides useful abstractions for credit assignment and policy learning. The TR preserves these benefits while relaxing unnecessary mathematical constraints, particularly the symmetry assumption on transition dynamics that may not reflect real-world problems.
For the broader AI research community, this work signals continued refinement of fundamental RL algorithms. As practical applications scale to higher-dimensional state spaces and longer horizons, reducing computational bottlenecks in representation learning directly impacts feasibility. The empirical evidence of lower computational overhead to learn, store, and use the TR makes this particularly relevant for resource-constrained environments and large-scale applications. The theoretical guarantee that TR is embedded in the top DR eigenvector provides confidence in the approach's validity without requiring practitioners to rebuild existing systems.
- →Terminal Representation achieves performance parity with Default Representation while requiring less computational overhead for learning and inference.
- →TR eliminates the mathematical requirement for symmetric transition dynamics, making it applicable to broader problem domains.
- →The representation can be learned as a lower-dimensional object, reducing both memory requirements and training complexity.
- →Theoretical embeddings prove TR captures equivalent knowledge to existing methods without eigendecomposition steps.
- →Applications span option discovery, reward shaping, transfer learning, and exploration—core components of modern RL systems.