Example Categorical DQN implementation with ReLAx
-
Updated
Aug 29, 2022 - Jupyter Notebook
Example Categorical DQN implementation with ReLAx
Solving CartPole using Distributional RL
Deep reinforcement learning framework for fast prototyping based on PyTorch
DRL-Router is a method based on distributional reinforcement learning for RSP problem。
Slide presentation reviewing advances in reinforcement learning
Simple Implementations of RL Algorithm in PyTorch
Distributional reinforcement learning algorithm using conjugated discrete distributions (C2D). Data and graphs for stochastic Atari 2600 environments.
[IROS 2023] Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learning
[UR 2023] Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment
Implementation of some of the Deep Distributional Reinforcement Learning Algorithms.
Reinforcement learning algorithm implementation
[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.
🐳 Implementation of various Distributional Reinforcement Learning Algorithms using TensorFlow2.
PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF) and Extensions: N-step Bootstrapping, PER, Noisy Layer, Dueling Networks, and parallelization.
Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.
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."