Research Explorer
Papers
Conferences
Authors
Topics
Keywords
Trends
Achievements
Explore
← Learning Types
Machine Learning
›
Learning Types
›
Model-Based RL
43 directly classified papers
Papers per year
2007: 2
2009: 2
2010: 2
2011: 1
2017: 1
2018: 3
2019: 5
2020: 5
2021: 3
2022: 8
2023: 6
2024: 3
2025: 2
Papers
StableToolBench-MirrorAPI: Modeling Tool Environments as Mirrors of 7,000+ Real-World APIs
ACL 2025
MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning
AAAI 2025
Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning
NIPS 2024
Simplifying Latent Dynamics with Softly State-Invariant World Models
NIPS 2024
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
NIPS 2024
Thinker: Learning to Plan and Act
NIPS 2023
Planning and Learning for Non-markovian Negative Side Effects Using Finite State Controllers
AAAI 2023
Posterior Sampling for Deep Reinforcement Learning
ICML 2023
Multi-Step Generalized Policy Improvement by Leveraging Approximate Models
NIPS 2023
Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning
AAAI 2023
Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models
EMNLP 2023
MoCoDA: Model-based Counterfactual Data Augmentation
NIPS 2022
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search
ICML 2022
Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic
AAAI 2022
Learning Probably Approximately Complete and Safe Action Models for Stochastic Worlds
AAAI 2022
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning
NIPS 2022
A Unified Framework for Alternating Offline Model Training and Policy Learning
NIPS 2022
Pareto Set Learning for Expensive Multi-Objective Optimization
NIPS 2022
Approximate Value Equivalence
NIPS 2022
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators
NIPS 2021
Weighted model estimation for offline model-based reinforcement learning
NIPS 2021
Minimax Model Learning
AISTATS 2021
Universal Value Iteration Networks: When Spatially-Invariant Is Not Universal
AAAI 2020
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
AAAI 2020
Generalized Hidden Parameter MDPs:Transferable Model-Based RL in a Handful of Trials
AAAI 2020
<
1
2
>