BY571

Soft-Actor-Critic-and-Extensions. PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.

CQL. PyTorch implementation of the Offline Reinforcement Learning algorithm CQL. Includes the versions DQN-CQL and SAC-CQL for discrete and continuous action spaces.

DQN-Atari-Agents. DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN

IQN-and-Extensions. PyTorch Implementation of Implicit Quantile Networks (IQN) for Distributional Reinforcement Learning with additional extensions like PER, Noisy layer, N-step bootstrapping, Dueling architecture and parallel env support.

Deep-Reinforcement-Learning-Algorithm-Collection. Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.

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

SAC_discrete. PyTorch implementation of the discrete Soft-Actor-Critic algorithm.

Munchausen-RL. PyTorch implementation of the Munchausen Reinforcement Learning Algorithms M-DQN and M-IQN

Implicit-Q-Learning. PyTorch implementation of the implicit Q-learning algorithm (IQL)

FQF-and-Extensions. 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.

QR-DQN. PyTorch implementation of QR-DQN: Distributional Reinforcement Learning with Quantile Regression

Normalized-Advantage-Function-NAF-. PyTorch implementation of the Q-Learning Algorithm Normalized Advantage Function for continuous control problems + PER and N-step Method

D4PG. 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.

DistRL-LLM. Distributed Reinforcement Learning for LLM Fine-Tuning with multi-GPU utilization

Randomized-Ensembled-Double-Q-learning-REDQ-. Pytorch implementation of Randomized Ensembled Double Q-learning (REDQ)

SCoRe. SCoRe: Training Language Models to Self-Correct via Reinforcement Learning

Medium_Code_Examples. Implementation of fundamental concepts and algorithms for reinforcement learning

GARNE-Genetic-Algorithm-with-Recurrent-Network-and-Novelty-Exploration. GARNE: Genetic-Algorithm-with-Recurrent-Network-and-Novelty-Exploration

Genetic-Algorithms-Neural-Network-Optimization. Genetic Algorithm for Neural Network Architecture and Hyperparameter Optimization and Neural Network Weight Optimization with Genetic Algorithm

GANs. ClusterGAN PyTorch implementation

OFENet. Jupyter Notebook

RA-PPO. PyTorch implementation of Risk-Averse Policy Learning

MBPO. Python

pytorch-vmpo. PyTorch implementation of V-MPO

Hindsight-Experience-Replay. Jupyter Notebook

PETS-MPC. Python

sft-kl-lora-trainer. Custom trl.SFTTrainer that adds a KL divergence loss between a LoRA-adapted model and its base model.

Agent-Tool-RL. Train SLM to use Tools with RL

Gym-Trading-Env. A simple, easy, customizable Open IA Gym environment for trading.

Udacity-DRL-Nanodegree-P3-Multiagent-RL-. Multi-Agent-RL Competition on Unitys Tennis Environment

CEN-Network. Jupyter Notebook

rl. A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

ARC-TTT. ARC-Test-Time-Training (ARC-TTT)

TD3-and-Extensions. PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradient (TD3) - including additional Extension to improve the algorithm's performance.

D4PG-ray. 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.

unsloth. Finetune Llama 3.3, Mistral, Phi-4, Qwen 2.5 & Gemma LLMs 2-5x faster with 70% less memory

Autonomous-Robocar. Self-Driving RC-Car

Particle-Filter-and-Localization. C++

CoT-Decoding. Minimal implementation of CoT-Decoding from the paper: Chain-of-Thought Reasoning without Prompting.

DRQN. Jupyter Notebook

TSMix. Jupyter Notebook

modular-rl. [ICML 2020] PyTorch Code for "One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control"