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IMPALA

16 papers tagged archive 2025-07-28

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.

PaperSource

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.

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.

TaskPapers
Reinforcement Learning10
Reinforcement Learning (RL)8
reinforcement-learning8
Atari Games2
Continuous Control2
Deep Reinforcement Learning2
Lifelong learning2
OpenAI Gym2
continuous-control2
Diagnostic1
Edge-computing1
Image Captioning1
MuJoCo1
Position1
Transfer Learning1

Usage over time archive 2025-07-28

Papers per year tagged with IMPALA: 2018 to 2025, peak 4 4 0 2018: 1 paper 2018 2019: 3 papers 2019 2020: 2 papers 2020 2021: 2 papers 2021 2022: 3 papers 2022 2023: 4 papers 2023 2024: 0 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (16 dated). Bars are counts, not a trend claim.

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

Distributed MethodsDistributed Reinforcement LearningPolicy Gradient Methods

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