Learning Task-relevant Sequence Representations via Intrinsic Dynamics Characteristics in Reinforcement Learning

The document introduces a novel Dynamic Feature-Driven Sequence Representation (DSR) method for visual deep reinforcement learning, which utilizes intrinsic dynamics characteristics to optimize state representation and achieve significant performance improvements in both benchmark tests and real-world autonomous driving scenarios.

Abstract

Learning task-relevant state representations is crucial to solving the problem of scene generalization in visual deep reinforcement learning. Prior work typically establishes a self-supervised auxiliary learner, introducing elements (e.g., rewards and actions) to extract task-relevant state information from observations through behavioral similarity metrics. However, the methods often ignore the inherent relationships between the elements (e.g., dynamics relationships) that are essential for learning accurate representations, and they are also limited to single-step metrics, which impedes the discrimination of short-term similar task/behavior information in long-term dynamics transitions. To solve the issues, we propose an intrinsic dynamic characteristics-driven sequence representation learning method (DSR) over a common DRL frame. Concretely, inspired by the fact of state transition in the underlying system, it constrains the optimization of the encoder via modeling the dynamics equations related to the state transition, which prompts the latent encoding information to satisfy the state transition process and thereby distinguishes state space and noise space. Further, to refine the ability of encoding similar tasks based on dynamics constraints, DSR also sequentially models inherent dynamics equation relationships from the perspective of sequence elements' frequency domain and multi-step prediction. Finally, experimental results show that DSR has achieved a significant performance boost in the Distracting DMControl Benchmark, with an average of 78.9% over the backbone baseline. Further results indicate that it also achieves the best performance in real-world autonomous driving tasks in the CARLA simulator. Moreover, the qualitative analysis results of t-SNE visualization validate that our method possesses superior representation ability on visual tasks.

Method

The method proposed in this paper is a sequence representation learning approach driven by intrinsic dynamics characteristics in reinforcement learning. It utilizes the dynamics equations related to the underlying state transition to derive complete state information from sequence observations, aiming to address the limitations of previous representation methods and enhance both representational capabilities and policy performance.

Main Finding

The main finding of this paper is the proposal of a novel method called Dynamic Feature-Driven Sequence Representation (DSR) for visual deep reinforcement learning, which leverages intrinsic dynamics characteristics to optimize state representation and significantly enhance policy performance in both benchmark tests and real-world autonomous driving scenarios. The method addresses the limitations of previous representation methods and demonstrates superior representation abilities through qualitative analysis, ultimately achieving substantial performance improvements.

Conclusion

The conclusion of this paper is that the proposed Dynamic Feature-Driven Sequence Representation (DSR) method significantly improves policy performance in visual deep reinforcement learning, achieving the best performance in challenging benchmarks and real-world autonomous driving scenarios. The method effectively addresses the limitations of previous representation methods and demonstrates outstanding representation abilities through qualitative analysis, ultimately enhancing both representational capabilities and policy performance.

Keywords

Learning Task-relevant Sequence Representations, Intrinsic Dynamics Characteristics, Reinforcement Learning, State Representation, Dynamic Feature-Driven Sequence Representation, DTFT, Discrete-Time Fourier Transform, Inverse Dynamics, Reward Prediction, Autonomous Driving, Sequence Optimization, Bisimulation Metric, Multi-step Optimization, Self-supervised Labels, Encoder Modeling, Dynamics Equations, State Transition, Sequence Methods, Global Temporal Features, Sequence Representation Learning.