RxR-Trained Agents with Tryout Controller in Unseen Complex Environments

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does the path efficiency of RxR-trained agents with the tryout controller scale with increasing complexity of unseen environments (e.g., larger maps, more obstacles) compared to R2R-trained agents. Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these. 12 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the path efficiency of RxR-trained agents with the tryout controller scale with increasing complexity of unseen environments (e.g., larger maps, more obstacles) compared to R2R-trained agents?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance