Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.
-
Updated
Mar 15, 2023 - Python
Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.
🐳 Implementation of various Distributional Reinforcement Learning Algorithms using TensorFlow2.
[IROS 2023] Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learning
[ICRA 2024] Decentralized Multi-Robot Navigation for Autonomous Surface Vehicles with Distributional Reinforcement Learning
PyTorch implementation of D4PG with the 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.
[UR 2023] Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment
Deep reinforcement learning framework for fast prototyping based on PyTorch
Reinforcement learning algorithm implementation
DRL-Router is a method based on distributional reinforcement learning for RSP problem。
Implementation of some of the Deep Distributional Reinforcement Learning Algorithms.
Distributional reinforcement learning algorithm using conjugated discrete distributions (C2D). Data and graphs for stochastic Atari 2600 environments.
Simple Implementations of RL Algorithm in PyTorch
Add a description, image, and links to the distributional-rl topic page so that developers can more easily learn about it.
To associate your repository with the distributional-rl topic, visit your repo's landing page and select "manage topics."