On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

count % of corpus

2 results for “BCQ”

EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RLcs.LG

Simplifies BCQ to match and outperform state-of-the-art in offline RL benchmarks.

problem Sample efficiency in offline reinforcement learning.

method Introduces EMaQ, a novel backup operator for offline RL.

result EMaQ matches and outperforms prior state-of-the-art in offline RL benchmarks.

reinforcement learning offline RL sample efficiency BCQ EMaQ

2020-07-21abs ↗pdf ↗

Overcoming Model Bias for Robust Offline Deep Reinforcement Learningcs.LG

MOOSE improves offline RL robustness by using dynamics models.

problem Low robustness of model-free offline RL algorithms in industrial settings.

method MOOSE uses dynamics models to assess policy performance, keeping policies within data support.

result MOOSE outperforms state-of-the-art model-free offline RL algorithms in robust performance.

Offline RL Model-free Robustness Dynamics models Policy performance

2020-08-12abs ↗pdf ↗