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dennybritz avatar

dennybritz/reinforcement-learning

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Reinforcement Learning

This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous agents. It serves as a research-oriented collection of implementations that cover fundamental decision-making strategies, including dynamic programming, temporal difference learning, and policy gradient methods.

The project distinguishes itself by offering specialized frameworks for deep reinforcement learning and structured decision modeling. It includes implementations for deep Q-learning that utilize neural networks, experience replay, and prioritized sampling to approximate action values in complex environments. Additionally, it provides a suite of solvers for Markov decision processes that compute optimal policies and value functions through iterative evaluation and improvement techniques.

The library supports a broad range of learning architectures, enabling the optimization of policies in both discrete and continuous action spaces. It facilitates the study of agent behavior through various estimation methods, such as Monte Carlo sampling and actor-critic architectures, which balance exploration and exploitation during the training process.

The repository is structured as a collection of Jupyter Notebooks, providing documented examples and implementations for testing and researching reinforcement learning algorithms.

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www.wildml.com/2016/10/learning-reinforcement-learning
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Features

  • Deep Q-Learning Implementations - Trains agents to approximate action values using neural networks, experience replay, and prioritized sampling.
  • Reinforcement Learning Algorithms - Provides a comprehensive library of reinforcement learning algorithms for training autonomous agents.
  • Deep Q-Learning Frameworks - Provides a set of tools for approximating action values using neural networks and experience replay.
  • Markov Decision Process Solvers - Computes optimal policies and value functions for Markov decision processes using iterative evaluation techniques.
  • Reinforcement Learning - Provides a research-oriented collection of reinforcement learning algorithms for studying agent behavior.
  • Actor-Critic Architectures - Implements actor-critic architectures to balance exploration and exploitation during agent training.
  • Policy Gradient Implementations - Provides architectures for optimizing decision-making policies directly in discrete and continuous action spaces.
  • Policy Gradient Optimizers - Updates policy parameters directly using gradient-based methods for decision-making tasks.
  • Temporal Difference Learning - Updates value estimates incrementally by comparing current predictions with subsequent observations.
  • Dynamic Programming Solvers - Computes optimal policies by systematically sweeping through state spaces to refine value estimates.
  • Experience Replay Buffers - Stores past agent transitions in a memory structure to break temporal correlations during training.
  • Policy Gradient Methods - Updates policy parameters directly using gradient-based architectures to handle decision-making tasks.
  • Monte Carlo Sampling Methods - Estimates state values by averaging total accumulated rewards from multiple complete episodes.
  • Temporal Difference Optimizers - Improves decision-making by updating action-value estimates incrementally through temporal difference methods.
  • Reinforcement Learning Value Estimators - Estimates action values in complex environments using deep learning architectures and prioritized sampling.
  • Reinforcement Learning - Collection of implementations for fundamental reinforcement learning algorithms.
  • Reinforcement Learning Environments - Educational implementations of fundamental reinforcement learning algorithms.
22,039 stars·6,135 forks·Jupyter Notebook·MIT·20 views

Star history

Star history chart for dennybritz/reinforcement-learningStar history chart for dennybritz/reinforcement-learning

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does dennybritz/reinforcement-learning do?

This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous agents. It serves as a research-oriented collection of implementations that cover fundamental decision-making strategies, including dynamic programming, temporal difference learning, and policy gradient methods.

What are the main features of dennybritz/reinforcement-learning?

The main features of dennybritz/reinforcement-learning are: Deep Q-Learning Implementations, Reinforcement Learning Algorithms, Deep Q-Learning Frameworks, Markov Decision Process Solvers, Reinforcement Learning, Actor-Critic Architectures, Policy Gradient Implementations, Policy Gradient Optimizers.

Which projects share features with dennybritz/reinforcement-learning?

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.… udacity/deep-reinforcement-learning — This project is a deep reinforcement learning curriculum providing educational materials and implementation exercises… 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… ljpzzz/machinelearning — This project is a machine learning implementation library featuring a collection of code examples that implement… andri27-ts/reinforcement-learning — This project is a collection of reinforcement learning implementations and educational materials written in Python. It…

Projects sharing features with Reinforcement Learning

These projects share indexed features with Reinforcement Learning. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • morvanzhou/reinforcement-learning-with-tensorflowMorvanZhou avatar

    MorvanZhou/Reinforcement-learning-with-tensorflow

    9,464View on GitHub↗

    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

    Pythona3cactor-criticasynchronous-advantage-actor-critic
    View on GitHub↗9,464
  • udacity/deep-reinforcement-learningudacity avatar

    udacity/deep-reinforcement-learning

    5,169View on GitHub↗

    This project is a deep reinforcement learning curriculum providing educational materials and implementation exercises for mastering neural network-based agents. It serves as a framework for building reference versions of value-based and policy-based methods to solve sequential decision problems. The project provides specific implementations for continuous control simulations and multi-agent reinforcement learning, where agents are trained to cooperate or compete in shared environments. It includes a policy gradient framework for optimizing agent behavior through methods such as REINFORCE. Ca

    Jupyter Notebookcross-entropyddpgdeep-reinforcement-learning
    View on GitHub↗5,169
  • tensorlayer/tensorlayertensorlayer avatar

    tensorlayer/TensorLayer

    7,384View on GitHub↗

    TensorLayer is a backend-agnostic tensor library and deep learning framework designed for building neural network architectures. It provides a neural network abstraction layer that allows model logic to run across different deep learning engines using high-level layers and model components. The project serves as a deep reinforcement learning toolkit for implementing policy-based, value-based, and actor-critic agents. It includes specialized tools for managing experience replay and gradient-based policy optimization to handle both discrete and continuous action spaces. To support reinforcemen

    Python
    View on GitHub↗7,384
  • packtpublishing/deep-reinforcement-learning-hands-onPacktPublishing avatar

    PacktPublishing/Deep-Reinforcement-Learning-Hands-On

    3,098View on GitHub↗

    This project serves as an educational resource and training framework for developing intelligent agents through deep reinforcement learning. It provides a collection of practical tutorials and code examples designed to teach the implementation of neural networks for solving complex decision-making tasks. By focusing on hands-on learning, the material guides users through the process of building autonomous systems that improve their performance through trial and error. The framework centers on the integration of standardized simulation environments, allowing agents to interact with diverse tas

    Python
    View on GitHub↗3,098
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