{"paper":{"title":"A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Gang Pan, Long Yang, Minhao Shi, Qian Zheng, Wenjia Meng","submitted_at":"2018-02-09T08:46:21Z","abstract_excerpt":"Recently, a new multi-step temporal learning algorithm, called $Q(\\sigma)$, unifies $n$-step Tree-Backup (when $\\sigma=0$) and $n$-step Sarsa (when $\\sigma=1$) by introducing a sampling parameter $\\sigma$. However, similar to other multi-step temporal-difference learning algorithms, $Q(\\sigma)$ needs much memory consumption and computation time. Eligibility trace is an important mechanism to transform the off-line updates into efficient on-line ones which consume less memory and computation time. In this paper, we further develop the original $Q(\\sigma)$, combine it with eligibility traces and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.03171","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}