Modularized Implementation of Deep RL Algorithms in PyTorch

A Torch Based RL Framework for Rapid Prototyping of Research Papers

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.

TensorFlow implementation of Deep RL (Reinforcement Learning) papers based on deep Q-learning (DQN)

RL based agent for atari games

DQN, Double DQN, Dueling Network

Reinforcement Learning Playground

Deep reinforcement learning agent

Deep Reinforcement Learning: Value-Based methods. An implementation of DQN, DDQN, Dueling Architectures, DQV, DQV-Max on the PyTorch Lightning framework.

Using the DRL algorithms put forward by Deepmind to play Atari 2600 Games with a comparison of algorithm performance

Open AI gym lunar-lander solution using Deep Q-Learning Network Architectures

Example Dueling DQN implementation with ReLAx

A university project where we implement and experiment with different Reinforcement Learning algorithms and trying to optimize the CartPole environment from OpenAI Gym.

Intelligent elevator scheduling using QR-DQN with Dueling Networks and Prioritized Experience Replay. Achieves ~38% improvement over SCAN baseline. Published at IEEE AICAPS 2026. DOI: 10.1109/AICAPS68631.2026.11452978

A RL agent that learns to play doom's deadly corridor based on DDQN and PER.