LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

2023-10-05 · Woojun Kim, Jeonghye Kim, Youngchul Sung

In this paper, a unified framework for exploration in reinforcement learning (RL) is proposed based on an option-critic model. The proposed framework learns to integrate a set of diverse exploration strategies so that the agent can adaptively select the most effective exploration strategy over time to realize a relevant exploration-exploitation trade-off for each given task. The effectiveness of the proposed exploration framework is demonstrated by various experiments in the MiniGrid and Atari environments.

📄 PDF Abstract BibTeX arXiv:2310.03342

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beanie00/lesson 공식 구현 pytorch

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reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Exploration in Reinforcement Learning with Deep Covering Options

2020-05-01 · ICLR 2020 1 · Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Konidaris

While many option discovery methods have been proposed to accelerate exploration in reinforcement learning, they are often heuristic. Recently, covering options was proposed to discover a set of options that provably red…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Multi-Agent Deep Reinforcement Learning with Human Strategies

2018-06-12 · Thanh Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi

Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

ACRE: Actor-Critic with Reward-Preserving Exploration

2023-08-14 · Neural Computing and Applications 2023 8 · A. Ch. Kapoutsis, D. I. Koutras, Ch. D. Korkas, E. B. Kosmatopoulos

While reinforcement learning (RL) algorithms have generated impressive strategies for a wide range of tasks, the performance improvements in continuous-domain, real-world problems do not follow the same trend. Poor explo…

continuous-control Continuous Control Reinforcement Learning (RL)

Multi-agent Deep Covering Skill Discovery

2022-10-07 · Jiayu Chen, Marina Haliem, Tian Lan, Vaneet Aggarwal

The use of skills (a.k.a., options) can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery methods have been proposed for individual…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

A Study of Value-Aware Eigenoptions

2025-07-12 · Harshil Kotamreddy, Marlos C. Machado arxiv

Options, which impose an inductive bias toward temporal and hierarchical structure, offer a powerful framework for reinforcement learning (RL). While effective in sequential decision-making, they are often handcrafted ra…

Reinforcement Learning