Towards continual reinforcement learning: A review and perspectives
In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
[HTML][HTML] Reinforcement learning for systems pharmacology-oriented and personalized drug design
RK Tan, Y Liu, L Xie - Expert opinion on drug discovery, 2022 - Taylor & Francis
Introduction Many multi-genic systemic diseases such as neurological disorders,
inflammatory diseases, and the majority of cancers do not have effective treatments yet …
inflammatory diseases, and the majority of cancers do not have effective treatments yet …
Factored adaptation for non-stationary reinforcement learning
Dealing with non-stationarity in environments (eg, in the transition dynamics) and objectives
(eg, in the reward functions) is a challenging problem that is crucial in real-world …
(eg, in the reward functions) is a challenging problem that is crucial in real-world …
Deep reinforcement learning amidst continual structured non-stationarity
As humans, our goals and our environment are persistently changing throughout our lifetime
based on our experiences, actions, and internal and external drives. In contrast, typical …
based on our experiences, actions, and internal and external drives. In contrast, typical …
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 …
Deep reinforcement learning amidst lifelong non-stationarity
As humans, our goals and our environment are persistently changing throughout our lifetime
based on our experiences, actions, and internal and external drives. In contrast, typical …
based on our experiences, actions, and internal and external drives. In contrast, typical …
Provably efficient primal-dual reinforcement learning for cmdps with non-stationary objectives and constraints
We consider primal-dual-based reinforcement learning (RL) in episodic constrained Markov
decision processes (CMDPs) with non-stationary objectives and constraints, which plays a …
decision processes (CMDPs) with non-stationary objectives and constraints, which plays a …
Bridging adaptive management and reinforcement learning for more robust decisions
From out-competing grandmasters in chess to informing high-stakes healthcare decisions,
emerging methods from artificial intelligence are increasingly capable of making complex …
emerging methods from artificial intelligence are increasingly capable of making complex …
Autonomous reinforcement learning: Formalism and benchmarking
Reinforcement learning (RL) provides a naturalistic framing for learning through trial and
error, which is appealing both because of its simplicity and effectiveness and because of its …
error, which is appealing both because of its simplicity and effectiveness and because of its …
Tempo adaptation in non-stationary reinforcement learning
We first raise and tackle a``time synchronization''issue between the agent and the
environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real …
environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real …