Causal Discovery with Reinforcement Learning
https://arxiv.org/abs/1906.04477

A causal framework for distribution generalization
https://arxiv.org/abs/2006.07433

In Search of Lost Domain Generalization
https://arxiv.org/abs/2007.01434

Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
http://proceedings.mlr.press/v144/sonar21a.html

Direct Advantage Estimation
https://arxiv.org/abs/2109.06093

links advantage function with causal effect as in rubin model

#classics

This year nobel prize in economics went to the Angrist and Imbens
https://www.nobelprize.org/prizes/economic-sciences/2021/summary/

so if you did not read about instrumental variables yet, i guess you should
https://www.jstor.org/stable/2291629

Shaking the foundations: delusions in sequence models for interaction and control
https://arxiv.org/abs/2110.10819

Learning to Induce Causal Structure https://arxiv.org/abs/2204.04875
#causality

hi there,

not a causality link, but still! we’ve got paper accepted at ICML2022 (Spotlight), so if you’re interested in offline rl — check it out
https://twitter.com/vladkurenkov/status/1534235675725381632

retweet appreciated 😈

On Calibration and Out-of-domain Generalization
https://arxiv.org/abs/2102.10395

👋

we finally released our offline RL library with SOTA algorithms, so if you're into this stuff, check it out

- single-file implementations
- benchmarked on D4RL datasets
- wandb reports with full metric logs (so that you don't need to rely on final performance tables)

https://github.com/corl-team/CORL

A Survey on Causal Reinforcement Learning
https://arxiv.org/abs/2302.05209
10 Feb 2023
—-
While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov Decision Process (MDP), Partially Observed Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment Regime (DTR). Moreover, we summarize the evaluation matrices and open sources while we discuss emerging applications, along with promising prospects for the future development of CRL.

Dutch Rudder as an Acyclic Causal Model

Reinforcement Learning from Passive Data via Latent Intentions
https://arxiv.org/abs/2304.04782

👉 Our new paper @ ICML 2023

https://arxiv.org/abs/2301.13616
https://github.com/tinkoff-ai/sac-rnd