{"url":"/method/seed-rl","slug":"seed-rl","name":"SEED RL","full_name":"SEED RL","full_name_withheld":false,"description_markdown":"**SEED** (Scalable, Efficient, Deep-RL) is a scalable reinforcement learning agent. It utilizes an architecture that features centralized inference and an optimized communication layer. SEED adopts two state of the art distributed algorithms, [IMPALA](https://paperswithcode.com/method/impala)/[V-trace](https://paperswithcode.com/method/v-trace) (policy gradients) and R2D2 ([Q-learning](https://paperswithcode.com/method/q-learning)).","description_state":"present","introduced_year":null,"introduced_by":{"title":"SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference","paper":"/paper/seed-rl-scalable-and-efficient-deep-rl-with-1","first_author":"Lasse Espeholt","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/seed-rl-scalable-and-efficient-deep-rl-with-1"},"source":{"url":"https://arxiv.org/abs/1910.06591v2","title":"SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Reinforcement Learning","area_id":"reinforcement-learning","collection":"Distributed Reinforcement Learning","url":"/methods/category/distributed-reinforcement-learning","pwc_aliases":[]},{"area":"General","area_id":"general","collection":"Distributed Methods","url":"/methods/category/distributed-methods","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/valan-vision-and-language-agent-navigation","title":"VALAN: Vision and Language Agent Navigation","date":"2019-12-06","arxiv_id":"1912.03241","n_code_links":1,"syntology":null},{"paper":"/paper/seed-rl-scalable-and-efficient-deep-rl-with-1","title":"SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference","date":"2019-10-15","arxiv_id":"1910.06591","n_code_links":2,"syntology":{"ran":0,"of":6,"unverified":6,"pointer_only":0}}],"papers_shown":2,"tasks":[{"task":"/task/reinforcement-learning","name":"Reinforcement Learning","papers":2},{"task":"/task/deep-reinforcement-learning","name":"Deep Reinforcement Learning","papers":1},{"task":"/task/q-learning","name":"Q-Learning","papers":1},{"task":"/task/reinforcement-learning-1","name":"Reinforcement Learning (RL)","papers":1},{"task":"/task/vision-and-language-navigation","name":"Vision and Language Navigation","papers":1},{"task":"/task/reinforcement-learning-2","name":"reinforcement-learning","papers":1}],"tasks_shown":6,"n_tasks":6,"usage_by_year":[{"year":"2019","papers":2}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/seed-rl"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}