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IMPALA
Introduced by Lasse Espeholt et al. in IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
IMPALA, or the Importance Weighted Actor Learner Architecture, is an off-policy actor-critic framework that decouples acting from learning and learns from experience trajectories using V-trace. Unlike the popular A3C-based agents, in which workers communicate gradients with respect to the parameters of the policy to a central parameter server, IMPALA actors communicate trajectories of experience (sequences of states, actions, and rewards) to a centralized learner. Since the learner in IMPALA has access to full trajectories of experience we use a GPU to perform updates on mini-batches of trajectories while aggressively parallelising all time independent operations.
This type of decoupled architecture can achieve very high throughput. However, because the policy used to generate a trajectory can lag behind the policy on the learner by several updates at the time of gradient calculation, learning becomes off-policy. The V-trace off-policy actor-critic algorithm is used to correct for this harmful discrepancy.
Papers archive 2025-07-28
16 shown of 16, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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World Model Agents with Change-Based Intrinsic Motivation 26 Mar 2025 · 1 repository · arXiv:2503.21047
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Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform 29 Sep 2023 · 1 repository · arXiv:2310.00036Syntology ran 4 of 8 samples · 4 unverified · 8 pointer-only (licence)
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Exploring the Promise and Limits of Real-Time Recurrent Learning 30 May 2023 · 1 repository · arXiv:2305.19044
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DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm 29 May 2023 · 0 repositories · arXiv:2305.18501
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Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks 18 May 2023 · 2 repositories · arXiv:2305.10997
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Lifelong Reinforcement Learning with Modulating Masks 21 Dec 2022 · 4 repositories · arXiv:2212.11110
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EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine 21 Jun 2022 · 3 repositories · arXiv:2206.10558Syntology ran 1 of 5 samples · 4 unverified
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Semantic Exploration from Language Abstractions and Pretrained Representations 8 Apr 2022 · 0 repositories · arXiv:2204.05080
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A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments 24 Oct 2021 · 0 repositories · arXiv:2110.12415
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MACRPO: Multi-Agent Cooperative Recurrent Policy Optimization 2 Sep 2021 · 1 repository · arXiv:2109.00882
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PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy Network 16 Mar 2020 · 1 repository · arXiv:2003.06959
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A Self-Tuning Actor-Critic Algorithm 28 Feb 2020 · 0 repositories · arXiv:2002.12928
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IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks 30 Nov 2019 · 0 repositories · arXiv:1912.00167
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TorchBeast: A PyTorch Platform for Distributed RL 8 Oct 2019 · 3 repositories · arXiv:1910.03552Syntology ran 4 of 9 samples · 5 unverified · 3 pointer-only (licence)
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Towards Combining On-Off-Policy Methods for Real-World Applications 24 Apr 2019 · 0 repositories · arXiv:1904.10642
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IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures 5 Feb 2018 · 24 repositories · arXiv:1802.01561Syntology ran 16 of 34 samples · 18 unverified · 3 pointer-only (licence)
Tasks archive 2025-07-28
15 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections