Simple Implementations of RL Algorithm in PyTorch
-
Updated
Nov 16, 2021 - Python
Simple Implementations of RL Algorithm in PyTorch
A repostiory to generate grid activation for an environment
Trying out a reinforcement learning algorithm that uses predictions of future states
Planning from Pixels with PlaNet
Behaviour Cloning of Cartpole Swing-up Policy with Model-Predictive Uncertainty Regularization (UW CSE571 Guided Project)
Zero-trial Model-based Imitation Learning with Partial Trajectory
This is the official PyTorch implementation of my Master thesis. The main goal of this work was to optimize latent dynamics models with unsupervised representation learning.
RLFlow: Optimising Neural Network Subgraph Transformation with World Models
A Reinforcement Learning library for solving custom environments
This repo contains implementations of algorithms such a Q-learning, SARSA, TD, Policy gradient
A model-based approach to novelty search in reinforcement learning
Model-Based Generative Adversarial Imitation Learning
Learning to paint using Model-based Deep Reinforcement Learning
Code for "Model-based Reinforcement Learning for Continuous Control with Posterior Sampling", ICML 2021
Reinforcement Learning Research Project - World models that are continuously updated as curious agents explore the environment - Course project for Foundations of Intelligent and Learning Agents(CS747)
Reimplementation of "An Object-Oriented Representation for Efficient RL"
PyTorch implementation of "Learning Stable Deep Dynamics Models" (https://papers.nips.cc/paper/9292-learning-stable-deep-dynamics-models), with extensions to controlled dynamical systems.
Add a description, image, and links to the model-based-rl topic page so that developers can more easily learn about it.
To associate your repository with the model-based-rl topic, visit your repo's landing page and select "manage topics."