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

How to best explore in domains with sparse, delayed, and deceptive rewards isan important open problem for reinforcement learning (RL). This paper considersone such domain, the recently-proposed multi-agent benchmark of Pommerman. Thisdomain is very challenging for RL --- past work has shown that model-free RLalgorithms fail to achieve significant learning without artificially reducingthe environment's complexity. In this paper, we illuminate reasons behind thisfailure by providing a thorough analysis on the hardness of random explorationin Pommerman. While model-free random exploration is typically futile, wedevelop a model-based automatic reasoning module that can be used for saferexploration by pruning actions that will surely lead the agent to death. Weempirically demonstrate that this module can significantly improve learning.

On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman