Model-Based RL

Improving Token-Based World Models with Parallel Observation Prediction ICML 2024 Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning ICML 2024 KOROL: Learning Visualizable Object Feature with Koopman Operator Rollout for Manipulation CoRL 2024 Learning Causal Dynamics Models in Object-Oriented Environments ICML 2024 Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics ICLR 2024 Learning Latent Dynamic Robust Representations for World Models ICML 2024 Learning to Play Atari in a World of Tokens ICML 2024 Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models CoRL 2024 Locality Sensitive Sparse Encoding for Learning World Models Online ICLR 2024 MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning ICLR 2024 Mastering Memory Tasks with World Models ICLR 2024 Maximum Entropy Model Correction in Reinforcement Learning ICLR 2024 Model-based Reinforcement Learning for Confounded POMDPs ICML 2024 Model-based Reinforcement Learning for Parameterized Action Spaces ICML 2024 Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL ICML 2024 Multi-Task Interactive Robot Fleet Learning with Visual World Models CoRL 2024 Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback ICML 2024 No-Regret Reinforcement Learning in Smooth MDPs ICML 2024 On Representation Complexity of Model-based and Model-free Reinforcement Learning ICLR 2024 PcLast: Discovering Plannable Continuous Latent States ICML 2024 Policy-conditioned Environment Models are More Generalizable ICML 2024 Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning ICLR 2024 Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning ICLR 2024 Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs COLT 2024 Prospective Side Information for Latent MDPs ICML 2024