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Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".

Ansible Collection for configuring a VXLAN Fabric using Direct to Controller (DTC) workflows.

An elegant PyTorch deep reinforcement learning library.

Massively Parallel Deep Reinforcement Learning. 🔥

Soft Actor-Critic with advanced features

Deep reinforcement learning algorithms implemented using PyTorch, including DQN、DDPG、PPO、SAC、DDQN、Noisy-DQN.

RL skeleton codebase with Hydra configs, Slurm/submitit, and MySQL/MariaDB logging.

PyTorch implementation of SAC (Soft Actor-Critic)

Deep Reinforcement Learning for mobile robot navigation in IR-SIM simulation. Using DRL (SAC, TD3, PPO, DDPG) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.

Reinforcement Learning : Autonomous parallel parking task. implementing SAC and DreamerV3's World Model on Highway-env

A simulation and training package for a directly controlled single-component rigid tool manipulating disks in a 2D environment. Uses PyBox2D for the simulation, pytorch for the training.

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3.

A Python package for accessing and processing NIED Hi-net seismic data.

Funções de Sistemas de Financiamento comparação Gráfica - SAC, PRICE, SAA - Matplotlib

Solving games with reinforcement learning

Deep RL implementations. DQN, SAC, DDPG, TD3, PPO and VPG implemented in pytorch. Tested Env: LunarLander-v2 and Pendulum-v0.

RL-Toolkit: A Research Framework for Robotics

SAC, PPO, A2C implementation on Mujoco environments : Humanoid-v4, Ant-v4, Cheetah-v4 . Includes reward manipulation.