Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.

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.

🐳 Implementation of various Distributional Reinforcement Learning Algorithms using TensorFlow2.

[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.

Reinforcement learning algorithm implementation

Implementation of some of the Deep Distributional Reinforcement Learning Algorithms.

[UR 2023] Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment

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

Solving CartPole using Distributional RL

Example Categorical DQN implementation with ReLAx

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

Author implementation of DSUP(q) algorithms from the NeurIPS 2024 paper "Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning"

Simple Implementations of RL Algorithm in PyTorch