Model-based Reinforcement Learning
169 papers with code • 0 benchmarks • 1 datasets
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Libraries
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POPGym
Most implemented papers
When to Trust Your Model: Model-Based Policy Optimization
Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data.
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
kchua/handful-of-trials
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NeurIPS 2018
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance.
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning
Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good performance.
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning
Finally, we assess the performance of the algorithm for learning motor controllers for a six legged autonomous underwater vehicle.
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
proppo/pipps_demo
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ICML 2018
Previously, the exploding gradient problem has been explained to be central in deep learning and model-based reinforcement learning, because it causes numerical issues and instability in optimization.
Machine Learning and System Identification for Estimation in Physical Systems
The main approach to estimation and learning adopted is optimization based.
Dynamics-Aware Unsupervised Discovery of Skills
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.
Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
JianGuanTHU/IRecGAN
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NeurIPS 2019
Reinforcement learning is well suited for optimizing policies of recommender systems.
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
MBRL-Lib is designed as a platform for both researchers, to easily develop, debug and compare new algorithms, and non-expert user, to lower the entry-bar of deploying state-of-the-art algorithms.
Temporal Predictive Coding For Model-Based Planning In Latent Space
VinAIResearch/TPC-tensorflow
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High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
