Reinforcement learning project. The objective is to learn an asymmetric distance function over states that will allow goal-pursuing.
-
Updated
Jun 29, 2021 - Python
Reinforcement learning project. The objective is to learn an asymmetric distance function over states that will allow goal-pursuing.
Project 2 of Udacity's Deep Reinforcement Learning NanoDegree
A RL agent that learns to play doom's deadly corridor based on DDQN and PER.
Reinforcement learning of point to point reaching
Third homework for the Reinforcement Learning course
reinforcement learning framework with pytorch
An implementation of Deep Q-Learning Network for solving a Unity environment that can navigate and collect bananas in a large, square world.
(Prioritized experience replay, random uniform replay) with tabular-Q for blind cliffwalk problem introduced as a motivating example in the publication Schaul et al., 2015
A Reinforcement Learning library for solving custom environments
Prioritized Experience Replay for Reinforcement Learning
A merge between OpenAI Baselines and Stable Baselines with increased focus on HER+DDPG and ease of use. Simply run the bash script to get started!
gym environnement to simulate the energetic behaviour of a real estate
PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradient (TD3) - including additional Extension to improve the algorithm's performance.
DQN, DDQN - using experience replay or prioritized experience replay
Deep Reinforcement Learning: Value-Based methods. An implementation of DQN, DDQN, Dueling Architectures, DQV, DQV-Max on the PyTorch Lightning framework.
Implementation of RL Algorithms with PyTorch.
PGuNN - Playing Games using Neural Networks
Deep RL for Pixel-based Environments
Add a description, image, and links to the prioritized-experience-replay topic page so that developers can more easily learn about it.
To associate your repository with the prioritized-experience-replay topic, visit your repo's landing page and select "manage topics."