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Model-based Reinforcement Learning

Papers

Showing 151175 of 708 papers

TitleStatusHype
Reinforcement Twinning: from digital twins to model-based reinforcement learning0
DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing0
The Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback0
Efficient Exploration in Continuous-time Model-based Reinforcement Learning0
Benchmark Generation Framework with Customizable Distortions for Image Classifier RobustnessCode0
Relational Object-Centric Actor-Critic0
TD-MPC2: Scalable, Robust World Models for Continuous ControlCode2
Mind the Model, Not the Agent: The Primacy Bias in Model-based RL0
Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery0
Value-Biased Maximum Likelihood Estimation for Model-based Reinforcement Learning in Discounted Linear MDPs0
MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations0
STORM: Efficient Stochastic Transformer based World Models for Reinforcement LearningCode1
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL0
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement LearningCode0
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning0
Multi-timestep models for Model-based Reinforcement Learning0
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language ModelsCode2
Amortized Network Intervention to Steer the Excitatory Point Processes0
Probabilistic Reach-Avoid for Bayesian Neural NetworksCode0
HarmonyDream: Task Harmonization Inside World ModelsCode1
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement LearningCode1
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation0
Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory SamplingCode1
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning0
Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning0
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