A review of off-policy evaluation in reinforcement learning
Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …
learning and has been recently applied to solve a number of challenging problems. In this …
A review of recent advances in empirical likelihood
Empirical likelihood is widely used in many statistical problems. In this article, we provide a
review of the empirical likelihood method, due to its significant development in recent years …
review of the empirical likelihood method, due to its significant development in recent years …
Optidice: Offline policy optimization via stationary distribution correction estimation
We consider the offline reinforcement learning (RL) setting where the agent aims to optimize
the policy solely from the data without further environment interactions. In offline RL, the …
the policy solely from the data without further environment interactions. In offline RL, the …
Empirical study of off-policy policy evaluation for reinforcement learning
We offer an experimental benchmark and empirical study for off-policy policy evaluation
(OPE) in reinforcement learning, which is a key problem in many safety critical applications …
(OPE) in reinforcement learning, which is a key problem in many safety critical applications …
VOCE: Variational optimization with conservative estimation for offline safe reinforcement learning
Offline safe reinforcement learning (RL) algorithms promise to learn policies that satisfy
safety constraints directly in offline datasets without interacting with the environment. This …
safety constraints directly in offline datasets without interacting with the environment. This …
Why so pessimistic? estimating uncertainties for offline rl through ensembles, and why their independence matters
K Ghasemipour, SS Gu… - Advances in Neural …, 2022 - proceedings.neurips.cc
Motivated by the success of ensembles for uncertainty estimation in supervised learning, we
take a renewed look at how ensembles of $ Q $-functions can be leveraged as the primary …
take a renewed look at how ensembles of $ Q $-functions can be leveraged as the primary …
Toward theoretical understandings of robust markov decision processes: Sample complexity and asymptotics
Toward theoretical understandings of robust Markov decision processes: Sample
complexity and asymptotics Page 1 The Annals of Statistics 2022, Vol. 50, No. 6, 3223–3248 …
complexity and asymptotics Page 1 The Annals of Statistics 2022, Vol. 50, No. 6, 3223–3248 …
A minimax learning approach to off-policy evaluation in confounded partially observable markov decision processes
We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes
(POMDPs), where the evaluation policy depends only on observable variables and the …
(POMDPs), where the evaluation policy depends only on observable variables and the …
Universal off-policy evaluation
When faced with sequential decision-making problems, it is often useful to be able to predict
what would happen if decisions were made using a new policy. Those predictions must …
what would happen if decisions were made using a new policy. Those predictions must …
Minimax value interval for off-policy evaluation and policy optimization
We study minimax methods for off-policy evaluation (OPE) using value functions and
marginalized importance weights. Despite that they hold promises of overcoming the …
marginalized importance weights. Despite that they hold promises of overcoming the …