Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.
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Updated
Oct 25, 2020 - Jupyter Notebook
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
Deep Reinforcement learning based tumour localisation
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.
The DDPG algorithm incorporates Actor-Critic Deep Learning Agent for solving continuous action reinforcement learning problems.
Pytorch implementation of Deep Deterministic Policy Gradients (DDPG)
Pytorch implementation of twin delayed deep deterministic policy gradients (TD3)
Deliverables relating to the Advanced Reinforcement Learning University Unit
Implementation of Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings (NeurIPS, 2021) in Python
Deep Reinforcement Learning: Continuous Control. Solve the Unity ML-Agents Reacher Environment.
PPO algorithm implemetation for TF 2.8.0
Pytorch implementation of Proximal Policy Optimization (PPO) for continuous action spaces
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