English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The practical usage of reinforcement learning agents is often bottlenecked bythe duration of training time. To accelerate training, practitioners often turnto distributed reinforcement learning architectures to parallelize andaccelerate the training process. However, modern methods for scalablereinforcement learning (RL) often tradeoff between the throughput of samplesthat an RL agent can learn from (sample throughput) and the quality of learningfrom each sample (sample efficiency). In these scalable RL architectures, asone increases sample throughput (i.e. increasing parallelization in IMPALA),sample efficiency drops significantly. To address this, we propose a newdistributed reinforcement learning algorithm, IMPACT. IMPACT extends IMPALAwith three changes: a target network for stabilizing the surrogate objective, acircular buffer, and truncated importance sampling. In discrete action-spaceenvironments, we show that IMPACT attains higher reward and, simultaneously,achieves up to 30% decrease in training wall-time than that of IMPALA. Forcontinuous control environments, IMPACT trains faster than existing scalableagents while preserving the sample efficiency of synchronous PPO.

IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks