:: Library Catalog

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Opis bibliograficzny
Główni autorzy:	Derstroff, Cedric, Brugger, Jannis, Blüml, Jannis, Mezini, Mira, Kramer, Stefan, Kersting, Kristian
Format:	Preprint
Wydane:	2024
Hasła przedmiotowe:	Artificial Intelligence
Dostęp online:	https://arxiv.org/abs/2402.08511
Etykiety:	Dodaj etykietę Nie ma etykietki, Dołącz pierwszą etykiete!

Podobne zapisy

Adaptable Hindsight Experience Replay for Search-Based Learning
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Wydane: (2025)

Neural-Guided Equation Discovery
od: Brugger, Jannis, i wsp.
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Deep Reinforcement Learning via Object-Centric Attention
od: Blüml, Jannis, i wsp.
Wydane: (2025)

Polynomial Regret Concentration of UCB for Non-Deterministic State Transitions
od: Cömer, Can, i wsp.
Wydane: (2025)

Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
od: Derstroff, Cedric, i wsp.
Wydane: (2023)

Boosting deep Reinforcement Learning using pretraining with Logical Options
od: Ye, Zihan, i wsp.
Wydane: (2026)

Representation Matters for Mastering Chess: Improved Feature Representation in AlphaZero Outperforms Switching to Transformers
od: Czech, Johannes, i wsp.
Wydane: (2023)

HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning
od: Delfosse, Quentin, i wsp.
Wydane: (2024)

OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments
od: Delfosse, Quentin, i wsp.
Wydane: (2023)

Better Decisions through the Right Causal World Model
od: Dillies, Elisabeth, i wsp.
Wydane: (2025)

Prompting Neural-Guided Equation Discovery Based on Residuals
od: Brugger, Jannis, i wsp.
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Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
od: Kramer, Stefan, i wsp.
Wydane: (2023)

Deep Reinforcement Learning Agents are not even close to Human Intelligence
od: Delfosse, Quentin, i wsp.
Wydane: (2025)

Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess
od: Helfenstein, Felix, i wsp.
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Monte Carlo Search Algorithms Discovering Monte Carlo Tree Search Exploration Terms
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Monte Carlo Tree Search with Boltzmann Exploration
od: Painter, Michael, i wsp.
Wydane: (2024)

OCALM: Object-Centric Assessment with Language Models
od: Kaufmann, Timo, i wsp.
Wydane: (2024)

CodeSSM: Towards State Space Models for Code Understanding
od: Verma, Shweta, i wsp.
Wydane: (2025)

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs
od: Sakharova, Marina, i wsp.
Wydane: (2025)

Evaluating and Mitigating Errors in LLM-Generated Web API Integrations
od: Maninger, Daniel, i wsp.
Wydane: (2025)

Problem Solving Through Human-AI Preference-Based Cooperation
od: Dutta, Subhabrata, i wsp.
Wydane: (2024)

Towards Understanding What State Space Models Learn About Code
od: Wu, Jiali, i wsp.
Wydane: (2026)

Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning
od: Wu, Mingze, i wsp.
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Analysis of Long Range Dependency Understanding in State Space Models
od: Ravikumar, Srividya, i wsp.
Wydane: (2026)

Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design
od: Zheng, Zhi, i wsp.
Wydane: (2025)

Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information
od: Struppek, Lukas, i wsp.
Wydane: (2025)

A Critical Study of What Code-LLMs (Do Not) Learn
od: Anand, Abhinav, i wsp.
Wydane: (2024)

How Susceptible are LLMs to Influence in Prompts?
od: Anagnostidis, Sotiris, i wsp.
Wydane: (2024)

Epistemic Monte Carlo Tree Search
od: Oren, Yaniv, i wsp.
Wydane: (2022)

Interpretable end-to-end Neurosymbolic Reinforcement Learning agents
od: Grandien, Nils, i wsp.
Wydane: (2024)

Learning from Less: Guiding Deep Reinforcement Learning with Differentiable Symbolic Planning
od: Ye, Zihan, i wsp.
Wydane: (2025)

Generalized Proof-Number Monte-Carlo Tree Search
od: Kowalski, Jakub, i wsp.
Wydane: (2025)

Proof Number Based Monte-Carlo Tree Search
od: Kowalski, Jakub, i wsp.
Wydane: (2023)

Towards a Characterisation of Monte-Carlo Tree Search Performance in Different Games
od: Soemers, Dennis J. N. J., i wsp.
Wydane: (2024)

Array-Based Monte Carlo Tree Search
od: Ragan, James, i wsp.
Wydane: (2025)

Lookahead Pathology in Monte-Carlo Tree Search
od: Nguyen, Khoi P. N., i wsp.
Wydane: (2022)

Twice Sequential Monte Carlo for Tree Search
od: Oren, Yaniv, i wsp.
Wydane: (2025)

Doubly Robust Monte Carlo Tree Search
od: Liu, Manqing, i wsp.
Wydane: (2025)

Making deep neural networks right for the right scientific reasons by interacting with their explanations
od: Schramowski, Patrick, i wsp.
Wydane: (2020)

Deep Classifier Mimicry without Data Access
od: Braun, Steven, i wsp.
Wydane: (2023)