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TimeLearning and Using Hierarchical Abstraction (State and/or Temporal)Learning Logic-Based Structured RepresentationsLearning Generalized Plans, Policies, or Domain Knowledge
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Poster Session 1
(02:30 PM - 03:00 PM)		#3: Contextual Pre-Planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning#4: Graph Neural Networks and Graph Kernels For Learning Heuristics: Is there a difference?#2: GOOSE: Learning Domain-Independent Heuristics
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#18: Learning How to Create Generalizable Hierarchies for Robot Planning#13: Quantized Local Independence Discovery for Fine-Grained Causal Dynamics Learning in Reinforcement Learning#12: Conservative World Models
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#22: Massively Scalable Inverse Reinforcement Learning in Google Maps#17: Learning Safe Action Models with Partial Observability#14: Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce
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#26: Learning Generalizable Symbolic Options for Transfer in Reinforcement Learning#27: Value Iteration with Value of Information Networks#24: Towards General-Purpose In-Context Learning Agents
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#31: Leveraging Behavioral Cloning for Representation Alignment in Cross-Domain Policy Transfer#29: Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures#25: Learning Interactive Real-World Simulators
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#38: Contrastive Abstraction for Reinforcement Learning#36: Towards More Likely Models for AI Planning#28: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines
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#40: Relating Goal and Environmental Complexity for Improved Task Transfer: Initial Results
#46: POMRL: No-Regret Learning-to-Plan with Increasing Horizons#32: COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
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#41: Learning Abstract World Models for Value-preserving Planning with Options#50: Exploiting Contextual Structure to Generate Useful Auxiliary Tasks#33: Explore to Generalize in Zero-Shot RL
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#43: Uncertainty-Aware Action Repeating Options#55: Learning Discrete Models for Classical Planning Problems#35: Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models
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Coffee Break
(03:00 PM - 03:30 PM)
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Poster Session 2
(03:30 PM - 04:00 PM)		#44: Contrastive Representations Make Planning Easy#57: Plansformer: Generating Symbolic Plans using Transformers#39: Robust Driving Across Scenarios via Multi-residual Task Learning
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#52: Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels#58: Stochastic Safe Action Model Learning#53: Inverse Reinforcement Learning with Multiple Planning Horizons
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#60: Agent-Centric State Discovery for Finite-Memory POMDPs#59: Understanding Representations Pretrained with Auxiliary Losses for Embodied Agent Planning#54: A Theoretical Explanation of Deep RL Performance in Stochastic Environments
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#63: Learning Generalizable Visual Task Through Interaction#69: Non-adaptive Online Finetuning for Offline Reinforcement Learning#56: Simple Data Sharing for Multi-Tasked Goal-Oriented Problems
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#66: Hierarchical Reinforcement Learning with AI Planning Models#75: Inductive Generalization in Reinforcement Learning from Specifications#61: MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
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#67: Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning#77: Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Stochastic Settings#64: Modeling Boundedly Rational Agents with Latent Inference Budgets
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#71: Work-in-Progress: Using Symbolic Planning with Deep RL to Improve Learning#80: Reasoning with Language Model is Planning with World Model#65: Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation
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#10: Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning#83: Targeted Uncertainty Reduction in Robust MDPs#76: Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI
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#84: Normalization Enhances Generalization in Visual Reinforcement Learning
#86: General and Reusable Indexical Policies and Sketches#78: PADDLE: Logic Program Guided Policy Reuse in Deep Reinforcement Learning
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#85: A Study of Generalization in Offline Reinforcement Learning#87: Learning AI-System Capabilities under Stochasticity#79: RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$
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#88: Reinforcement Learning with Augmentation Invariant Representation: A Non-contrastive Approach
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