Neuro-Symbolic RL

RL: Reinforcement Learning

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16 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Internalizing Investment Mandates: A Neurosymbolic RL Approach to Portfolio Allocation
Nov 12, 2026Zeynep Yıldız, Satchit Chatterji, Ercan Atam +1Constrained RL Neuro-Symbolic RL
Boğaziçi University, Istanbul, Türkiye · Finesse Lab, University of Amsterdam, Amsterdam, the Netherlands
Using Transformers for Teaching and Learning via Symbolic Distillation of Neural A-life Agents
Nov 5, 2026Shuwei Wang, Vadim Bulitko, Matthew R. G. Brown +5Policy Distillation Artificial Life
Department of Computing Science, University of Alberta, 116 Street and 85 Avenue, Edmonton, AB, T6G 2R3, Canada · Filuta AI, 1606 Headway Circle, Austin, TX 78754, USA · Department of Computer Science, Data Science, University of British Columbia, 6200 University Blvd, Vancouver, BC Canada V6T 1Z4, Canada +2
Interpretable Meta-Policies for Neural Decision Making
Oct 27, 2026Raban Emunds, Jannis Blüml, Quentin Delfosse +1Interpretability in RL Neuro-Symbolic RL
Department of Computer Science, Technical University Darmstadt, Germany · Hessian Center for Artificial Intelligence (hessian.AI) · Centre for Cognitive Science, Darmstadt +1
Managing Action Preconditions in Neuro-Symbolic RL: Three Placement Strategies for Embodied Agents
Sep 13, 2026Norbert Oswald, Fabian Deuser, Thomas BräunlNeuro-Symbolic RL Reinforcement Learning
Institute of Distributed Intelligent Systems University of the Bundeswehr Munich Munich, Germany · School of Engineering The University of Western Australia Perth, Australia
Towards Zero-Shot Task Transfer with Neurosymbolic World Models
Aug 18, 2026Isidoro Tamassia, Lennert De Smet, Giuseppe MarraZero-Shot RL World Model Learning
KU Leuven, Department of Computer Science, Belgium
Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning
Aug 4, 2026Subrat Prasad Panda, Blaise Genest, Arvind EaswaranReinforcement Learning Symbolic Planning
CCDS, NTU Singapore · IPAL, CNRS, France
Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability
Jul 20, 2026Taewoon Kim, Vincent François-Lavet, Michael CochezReinforcement Learning Partially Observable RL
HumemAI, Amsterdam, The Netherlands · Vrije Universiteit Amsterdam, Amsterdam, The Netherlands · ELLIS Institute Finland & Åbo Akademi University, Turku, Finland
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
Jul 14, 2026Jonas Ehrhardt, René Heesch, Oliver NiggemannReinforcement Learning Constrained RL
Helmut-Schmidt-University, Hamburg, Germany
Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies
Jun 6, 2026Ashkan Ansarifard, Matteo Mancanelli, Elena Umili +1Reinforcement Learning Transformer-Based RL
Sapienza University of Rome · University of Rome La Sapienza, Rome, Italy
Learning Explicit Behavioral Models with Adaptive Questions and World-Model Probes
Jun 5, 2026Hikaru Shindo, Yu Deng, Teng Cao +5Reinforcement Learning World Models
1Artificial Intelligence and Machine Learning Lab, Technical University of Darmstadt, Germany · Department of Computer Science, Technical University of Darmstadt, Germany · 2Hessian Center for Artificial Intelligence (hessian.AI), Germany +3
From Passive Reuse to Active Reasoning: Grounding Large Language Models for Neuro-Symbolic Experience Replay
May 10, 2026Yanan Xiao, Yixiang Tang, Zechen Feng +3Reinforcement Learning Experience Replay
1Affiliation 1 · 2Affiliation 2 · 3Affiliation 3
LANTERN: LLM-Augmented Neurosymbolic Transfer with Experience-Gated Reasoning Networks
May 6, 2026Mahyar Alinejad, Yue Wang, Amrit Singh Bedi +1Reinforcement Learning LLM-Guided RL
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida, USA · Department of Computer Science, University of Central Florida, Orlando, Florida, USA
Sample-efficient Neuro-symbolic Proximal Policy Optimization
Apr 28, 2026Simone Murari, Celeste Veronese, Daniele MeliReinforcement Learning Proximal Policy Optimization
Dept. of Computer Science, University of Verona, Italy
GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning
Apr 18, 2026Hikaru Shindo, Henri Rößler, Quentin Delfosse +1Reinforcement Learning LLM-Guided RL
Technical University of Darmstadt · Google Intrinsic · German Research Center for Artificial Intelligence (DFKI) +1
Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning
Mar 11, 2026Hong Lu, Pierrick Lorang, Timothy R. Duggan +2Robotics Neuro-Symbolic Reasoning
Department of Computer Science, Tufts University, Medford, MA, USA · Austrian Institute of Technology GmbH, Vienna, Austria
Boosting deep Reinforcement Learning using pretraining with Logical Options
Mar 6, 2026Zihan Ye, Phil Chau, Raban Emunds +5Neuro-Symbolic RL Reinforcement Learning
CS Department, Technical University of Darmstadt, Germany · AIML Group · Hessian AI +2

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Internalizing Investment Mandates: A Neurosymbolic RL Approach to Portfolio Allocation

Zeynep Yıldız, Satchit Chatterji, Ercan Atam, Erman Acar

Boğaziçi University, Istanbul, Türkiye · Finesse Lab, University of Amsterdam, Amsterdam, the Netherlands

Constrained RL Neuro-Symbolic RL Portfolio Optimization

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Institutional portfolios operate under investment policy statements that specify asset allocation constraints, often conditioned on market regime information. Deep reinforcement learning has been shown to optimize financial performance but typically does not enforce rule compliance, making it unsuitable for real-world deployment. Existing neurosymbolic methods such as Probabilistic Logic Policy Gradient (PLPG) offer constraint-satisfying reinforcement learning (RL) via differentiable symbolic supervision but assume a finite, discrete action space. We present a PLPG-inspired continuous allocation method that estimates the probability that the policy's own executed action satisfies the investment mandate, using reparameterized Monte Carlo sampling, and adds it to the training objective. No post-hoc enforcement mechanism intercepts actions at inference time and compliance is learned by the agent. Audited directly on deterministic mean actions with no correction, the policy reduces violations of a six-rule regime-conditional mandate from 96-98% for unconstrained RL and 33.4% for a reward-penalty baseline to 4.8%, showing that a continuous-action neurosymbolic policy can substantially internalize a symbolic mandate.