Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
[Uczenie maszynowe] Autorzy: Mathias Jackermeier, Jacques Cloete, Alessandro Abate
Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides a precise and structured formalism for specifying instructions to agents, and has been successfully adopted for training generalist multi-task policies. However, differences in implementations, task distributions, and evaluation protocols make existing methods difficult to compare, while high computational costs limit the scale and statistical reliability of experiments. We introduce Jaxolotl, a unified high-performance benchmark suite for multi-task LTL-RL to address these concerns. Jaxolotl provides a modular, end-to-end JAX implementation of six representative algorithms and four environments, together with newly curated task suites and a standardised, statistically robust evaluation protocol. By precompiling symbolic task representatio…
- Kategoria arxiv
- cs.LG
- Kategoria
- Uczenie maszynowe