Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents

Researchers propose simplicial embeddings, a lightweight geometric technique that constrains neural network representations to discrete, sparse structures, improving sample efficiency in reinforcement learning agents. When integrated into popular actor-critic algorithms like PPO and FastTD3, the method enhances performance and learning speed across diverse control tasks without sacrificing computational speed.

This research addresses a persistent challenge in deep reinforcement learning: the tension between computational efficiency and sample efficiency. While recent advances have scaled actor-critic training through massive environment parallelization, agents still consume enormous quantities of interaction data to reach target performance levels. The proposed simplicial embeddings approach introduces a geometric constraint that forces learned representations into lower-dimensional simplicial structures, naturally producing sparse and discrete features.

The technical contribution builds on established principles that well-structured representations improve generalization in RL systems. By imposing this inductive bias at the embedding layer, the method stabilizes critic bootstrapping—a critical component where temporal difference learning can diverge—while simultaneously strengthening policy gradient estimates. The approach demonstrates broad applicability across multiple algorithm families (FastTD3, FastSAC, PPO) and control paradigms (continuous and discrete).

For the AI development community, this work carries practical significance. Sample efficiency remains a bottleneck in real-world RL applications where environment interaction is costly, whether through simulations requiring computational resources or physical robots requiring time and maintenance. The demonstrated improvements come without runtime penalties, making adoption straightforward for existing systems.

Looking ahead, the critical evaluation metric will be empirical validation across increasingly complex, realistic environments. The geometric interpretation of simplicial structures may also inspire further theoretical work connecting representation topology to learning dynamics, potentially yielding additional architectural innovations for deep RL agents.

• →Simplicial embeddings introduce geometric constraints that generate sparse, discrete features stabilizing reinforcement learning training.
• →Method improves sample efficiency across FastTD3, FastSAC, and PPO without degrading computational runtime.
• →Approach addresses the fundamental challenge of reducing environment interactions needed for performance convergence.
• →Technique applies across both continuous and discrete control domains, demonstrating generalizability.
• →Work suggests representation topology significantly impacts policy gradient and bootstrapping stability in deep RL.