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This project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow. It provides a practical codebase for both model-free and model-based learning agents, designed to demonstrate how AI agents learn through trial and error. The collection features detailed implementations of various algorithmic approaches, including Deep Q-Networks and Policy Gradient methods. It specifically covers Actor-Critic architectures for continuous and discrete action spaces, alongside Proximal Policy Optimization and Deep Deterministic Policy Gradients. The framewor
This project is a PyTorch reinforcement learning library and agent training framework. It provides a suite of deep reinforcement learning algorithms, including DQN, PPO, and SAC, to facilitate the development of autonomous agents that optimize behavior through trial and error. The library focuses on the implementation of various actor-critic methods and deep learning architectures for research into autonomous decision making. It enables the training of intelligent agents within diverse environments by leveraging PyTorch-based model implementations. The codebase covers core reinforcement lear
This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg
This project provides a comprehensive technical guide and framework for engineering large-scale machine learning systems. It covers the full lifecycle of model development, focusing on the infrastructure and computational principles required to build, train, and serve generative AI models across distributed GPU clusters. The repository distinguishes itself by offering deep-dive tutorials and implementation strategies for complex system challenges. It emphasizes high-performance architectural primitives, such as collective communication orchestration, distributed tensor sharding, and static gr
DRL-robot-navigation is a deep reinforcement learning platform and robotic simulation framework designed to train autonomous mobile robots for collision-free path planning. It uses neural network policies and physics-engine simulation environments to teach robots how to navigate toward target coordinates while avoiding obstacles.
The main features of reiniscimurs/drl-robot-navigation are: Reinforcement Learning Environments, Continuous Control Training, Deep Reinforcement Learning Implementations, Goal-Directed, Reward Functions, Policy Gradient Optimizers, Reinforcement Learning, ROS Simulation Bridges.
Projects with overlapping indexed features include: morvanzhou/reinforcement-learning-with-tensorflow — This project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow.… sweetice/deep-reinforcement-learning-with-pytorch — This project is a PyTorch reinforcement learning library and agent training framework. It provides a suite of deep… lazyprogrammer/machine_learning_examples — This project is a comprehensive collection of practical code examples and implementation libraries for machine… zhaochenyang20/awesome-ml-sys-tutorial — This project provides a comprehensive technical guide and framework for engineering large-scale machine learning… tensorlayer/tensorlayer — TensorLayer is a backend-agnostic tensor library and deep learning framework designed for building neural network… packtpublishing/deep-reinforcement-learning-hands-on — This project serves as an educational resource and training framework for developing intelligent agents through deep…