3 repositorios
Awesome GitHub RepositoriesDeep Deterministic
Specific implementations of the DDPG algorithm for continuous action spaces.
Distinct from Policy Gradient Implementations: Specializes the general Policy Gradient Implementations into the specific DDPG algorithm.
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Awesome Deep Deterministic GitHub Repositories
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- morvanzhou/reinforcement-learning-with-tensorflow
MorvanZhou/Reinforcement-learning-with-tensorflow
9,464Ver en GitHubThis 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
Implements the Deep Deterministic Policy Gradient algorithm to handle continuous action spaces.
Pythona3cactor-criticasynchronous-advantage-actor-critic - vwxyzjn/cleanrl
vwxyzjn/cleanrl
9,127Ver en GitHubCleanRL is a reinforcement learning library and PyTorch framework providing a suite of reproducible implementations for online reinforcement learning algorithms. It serves as a deep reinforcement learning benchmark suite and experiment orchestrator designed for research and agent development across both discrete and continuous action spaces. The project is distinguished by its single-file algorithm implementation approach, which encapsulates each algorithm in a standalone script to eliminate complex class hierarchies. This structure is paired with a system for scheduling and executing large-s
Provides a deterministic policy implementation for continuous action spaces using an actor-critic architecture.
Pythona2cactor-criticadvantage-actor-critic - ljpzzz/machinelearning
ljpzzz/machinelearning
8,706Ver en GitHubThis project is a machine learning implementation library featuring a collection of code examples that implement supervised, unsupervised, and reinforcement learning algorithms from scratch. It provides a comprehensive set of toolkits for core machine learning components, including a natural language processing toolkit, a reinforcement learning framework, and suites for data dimensionality reduction and pattern mining. The library includes specialized implementations for reinforcement learning, such as Q-Learning, Deep Q-Networks, and Actor-Critic agents. The natural language processing capab
Implements the DDPG algorithm for solving reinforcement learning problems in continuous action spaces.
Jupyter Notebookalgorithmsmachinelearningreinforcementlearning
