Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.

Upside-Down Reinforcement Learning (⅂ꓤ) implementation in PyTorch. Based on the paper published by Jürgen Schmidhuber.

Implementing Deep Reinforcement Learning Algorithms in Python for use in the MuJoCo Physics Simulator

The DDPG algorithm incorporates Actor-Critic Deep Learning Agent for solving continuous action reinforcement learning problems.

Deep Reinforcement learning based tumour localisation

Deliverables relating to the Advanced Reinforcement Learning University Unit

This repository contains an implementation of Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm designed for environments with continuous action spaces. It features actor-critic architecture, experience replay, and exploration strategies, and is tested on environments like MountainCarContinuous. More info on Medium blog!

Pytorch implementation of Deep Deterministic Policy Gradients (DDPG)

Distributed PyTorch implementation of D4PG with ray. Using a SOTA IQN Critic instead of C51. Implementation includes also the extensions Munchausen RL and D2RL which can be added to D4PG to improve its performance.

Pytorch implementation of Proximal Policy Optimization (PPO) for continuous action spaces

Pytorch implementation of twin delayed deep deterministic policy gradients (TD3)

Deep Reinforcement Learning: Continuous Control. Solve the Unity ML-Agents Reacher Environment.

Implementation of Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings (NeurIPS, 2021) in Python

PPO algorithm implemetation for TF 2.8.0