Simplicial Embeddings Improve Sample Efficiency

Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents

arXiv:2510.13704v2 Announce Type: replace Abstract: Recent works have proposed accelerating the wall-clock training time of actor-critic methods via the use of large-scale environment parallelization; unfortunately, these can sometimes still require large number of environment interactions to achieve a desired level of performance. Noting that well-structured representations can improve the generalization and sample efficiency of deep reinforcement learning (RL) agents, we propose the use of...