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Exploration in deep reinforcement learning: A survey
This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …
techniques are of primary importance when solving sparse reward problems. In sparse …
Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
T Lesort, V Lomonaco, A Stoian, D Maltoni, D Filliat… - Information fusion, 2020 - Elsevier
Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …
distribution and learning objective change through time, or where all the training data and …
Guiding pretraining in reinforcement learning with large language models
Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped
reward function. Intrinsically motivated exploration methods address this limitation by …
reward function. Intrinsically motivated exploration methods address this limitation by …
Off-policy deep reinforcement learning without exploration
S Fujimoto, D Meger, D Precup - … conference on machine …, 2019 - proceedings.mlr.press
Many practical applications of reinforcement learning constrain agents to learn from a fixed
batch of data which has already been gathered, without offering further possibility for data …
batch of data which has already been gathered, without offering further possibility for data …
Go-explore: a new approach for hard-exploration problems
A Ecoffet, J Huizinga, J Lehman, KO Stanley… - arxiv preprint arxiv …, 2019 - arxiv.org
A grand challenge in reinforcement learning is intelligent exploration, especially when
rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard …
rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard …
Skew-fit: State-covering self-supervised reinforcement learning
Autonomous agents that must exhibit flexible and broad capabilities will need to be
equipped with large repertoires of skills. Defining each skill with a manually-designed …
equipped with large repertoires of skills. Defining each skill with a manually-designed …
Cooperative exploration for multi-agent deep reinforcement learning
Exploration is critical for good results in deep reinforcement learning and has attracted much
attention. However, existing multi-agent deep reinforcement learning algorithms still use …
attention. However, existing multi-agent deep reinforcement learning algorithms still use …
Reconciling novelty and complexity through a rational analysis of curiosity.
R Dubey, TL Griffiths - Psychological Review, 2020 - psycnet.apa.org
Curiosity is considered to be the essence of science and an integral component of cognition.
What prompts curiosity in a learner? Previous theoretical accounts of curiosity remain …
What prompts curiosity in a learner? Previous theoretical accounts of curiosity remain …
Evolution-guided policy gradient in reinforcement learning
Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to
a range of challenging control tasks. However, these methods typically suffer from three core …
a range of challenging control tasks. However, these methods typically suffer from three core …
Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges
SK Das, F Benkhelifa, Y Sun, H Abumarshoud… - Computer Networks, 2023 - Elsevier
Sixth generation (6G) internet of things (IoT) networks will modernize the applications and
satisfy user demands through implementing smart and automated systems. Intelligence …
satisfy user demands through implementing smart and automated systems. Intelligence …