This presentation contains very precise yet detailed explanation of concepts of a very interesting topic -- Reinforcement Learning.
-
Updated
Dec 25, 2017
This presentation contains very precise yet detailed explanation of concepts of a very interesting topic -- Reinforcement Learning.
Simple Implementations of RL Algorithm in PyTorch
Model-based reinforcement learning using CEM, MPC and PETS
Example MBPO implementation with ReLAx
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.
A model-based approach to novelty search in reinforcement learning
Example FRWR (PDDM) implementation with ReLAx
Behaviour Cloning of Cartpole Swing-up Policy with Model-Predictive Uncertainty Regularization (UW CSE571 Guided Project)
Codes for "Efficient Offline Policy Optimization with a Learned Model", ICLR2023
VQ-VAE-based image tokenizer for model-based RL
A repostiory to generate grid activation for an environment
A multi-agent deep reinforcement learning model to de-traffic our lives
Control of InvertedPendulum on a cart
Trying out a reinforcement learning algorithm that uses predictions of future states
Sample efficient model based RL algorithm
RLFlow: Optimising Neural Network Subgraph Transformation with World Models
Model-Based Generative Adversarial Imitation Learning
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."