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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] Review of online learning for control and diagnostics of power converters and drives: Algorithms, implementations and applications
Power converters and motor drives are playing a significant role in the transition towards
sustainable energy systems and transportation electrification. In this context, rich diversity of …
sustainable energy systems and transportation electrification. In this context, rich diversity of …
Adversarially trained actor critic for offline reinforcement learning
CA Cheng, T ** for uncertainty-driven offline reinforcement learning
Offline Reinforcement Learning (RL) aims to learn policies from previously collected
datasets without exploring the environment. Directly applying off-policy algorithms to offline …
datasets without exploring the environment. Directly applying off-policy algorithms to offline …
Pessimistic q-learning for offline reinforcement learning: Towards optimal sample complexity
Offline or batch reinforcement learning seeks to learn a near-optimal policy using history
data without active exploration of the environment. To counter the insufficient coverage and …
data without active exploration of the environment. To counter the insufficient coverage and …
Hybrid rl: Using both offline and online data can make rl efficient
We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has
access to an offline dataset and the ability to collect experience via real-world online …
access to an offline dataset and the ability to collect experience via real-world online …
Pessimistic model-based offline reinforcement learning under partial coverage
We study model-based offline Reinforcement Learning with general function approximation
without a full coverage assumption on the offline data distribution. We present an algorithm …
without a full coverage assumption on the offline data distribution. We present an algorithm …
Settling the sample complexity of model-based offline reinforcement learning
Settling the sample complexity of model-based offline reinforcement learning Page 1 The
Annals of Statistics 2024, Vol. 52, No. 1, 233–260 https://doi.org/10.1214/23-AOS2342 © …
Annals of Statistics 2024, Vol. 52, No. 1, 233–260 https://doi.org/10.1214/23-AOS2342 © …
Reinforcement learning with human feedback: Learning dynamic choices via pessimism
In this paper, we study offline Reinforcement Learning with Human Feedback (RLHF) where
we aim to learn the human's underlying reward and the MDP's optimal policy from a set of …
we aim to learn the human's underlying reward and the MDP's optimal policy from a set of …