Model-based Reinforcement Learning
112 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find Model-based Reinforcement Learning models and implementationsMost implemented papers
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.
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.
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.
Model-Based Reinforcement Learning for Atari
We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.
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.
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.
