State of the Art of Reinforcement Learning

2022-01-17 · ICLR Track Blog 2022 5 · Anonymous

Yeah so having offered introductions, the focus of this paper will now be to offer a few quick discussions surrounding reinforcement learning domain papers published at next week’s ICLR conference. To keep the project manageable we’ll limit our attention to those papers chosen for spotlight and oral presentations, which is a proxy for selecting papers deemed to have the most significant findings by conference chairs. In some cases the discussions may be more involved than others, this is partly indicative of my limited background in the field as in a few cases may be out of my depth. Hopefully a reader may expect to pick up a few points about current state of the art for the field. Yeah so without further ado.

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reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Episodic Reinforcement Learning with Associative Memory

2020-05-01 · ICLR 2020 1 · Guangxiang Zhu*, Zichuan Lin*, Guangwen Yang, Chongjie Zhang

Sample efficiency has been one of the major challenges for deep reinforcement learning. Non-parametric episodic control has been proposed to speed up parametric reinforcement learning by rapidly latching on previously su…

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Non-Robust Feature Mapping in Deep Reinforcement Learning

2021-06-18 · ICML Workshop AML 2021 7 · Ezgi Korkmaz

Adversarial perturbations to state observations can dramatically degrade the performance of deep reinforcement learning policies, and thus raise concerns regarding the robustness of deep reinforcement learning agents. A …

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The complexity of non-stationary reinforcement learning

2023-07-13 · Christos Papadimitriou, Binghui Peng

The problem of continual learning in the domain of reinforcement learning, often called non-stationary reinforcement learning, has been identified as an important challenge to the application of reinforcement learning. W…

Continual Learning reinforcement-learning Reinforcement Learning

Generalized Maximum Entropy Reinforcement Learning via Reward Shaping

2021-09-29 · Feng Tao, Yongcan Cao

Entropy regularization is a commonly used technique in reinforcement learning to improve exploration and cultivate a better pre-trained policy for later adaptation. Recent studies further show that the use of entropy reg…

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Safe Continual Reinforcement Learning Methods for Nonstationary Environments. Towards a Survey of the State of the Art

2026-01-08 · Timofey Tomashevskiy arxiv

This work provides a state-of-the-art survey of continual safe online reinforcement learning (COSRL) methods. We discuss theoretical aspects, challenges, and open questions in building continual online safe reinforcement…

Reinforcement Learning Continual Learning