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udacity/deep-reinforcement-learning

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www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893↗

Deep Reinforcement Learning

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.

Capabilities cover a wide range of optimization algorithms, including deep Q-learning, deterministic policy gradients, and dynamic programming for Markov Decision Process modeling. The system supports various training domains such as robotic navigation, financial trading automation, and physics-based simulations.

The materials are delivered as a series of Jupyter Notebooks.

Features

  • RL Agent Implementation Frameworks - Provides a structural framework for building reference versions of value-based and policy-based reinforcement learning agents.
  • Reinforcement Learning Curricula - Provides a structured learning path and implementation exercises specifically for the study of reinforcement learning.
  • Actor-Critic Architectures - Implements architectures that combine policy-based agents with value-based evaluators to balance exploration and exploitation.
  • Deterministic Policy Gradients - Implements methods for optimizing continuous actions by predicting deterministic values based on critic network gradients.

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5,169 stars·2,378 forks·Jupyter Notebook·MIT·38 views
  • Deep Q-Learning Implementations - Implements deep Q-learning algorithms using neural networks to estimate optimal action-value functions.
  • DDPG Implementations - Implements the Deep Deterministic Policy Gradient algorithm for solving continuous control tasks in physics environments.
  • Policy Gradient Implementations - Provides a framework for optimizing decision-making policies using gradient-based methods like REINFORCE.
  • Policy Gradient Optimizers - Implements gradient-based methods for updating policy parameters to maximize expected rewards in various action spaces.
  • REINFORCE Implementations - Implements the REINFORCE algorithm to optimize neural network action probabilities using episodic reward accumulation.
  • Neural Action-Value Estimation - Uses neural networks to approximate action-value functions for complex state spaces in simulated environments.
  • Agent Implementations - Provides practical code implementations for building autonomous agents that select actions and learn from experience.
  • Temporal Difference Learning - Implements algorithms that update value estimates incrementally by comparing current predictions with subsequent observations.
  • Multi-Agent Training - Provides training frameworks that coordinate multiple agents to cooperate or compete within shared simulated environments.
  • Continuous Control Training - Implements training for agents in high-dimensional, real-valued action spaces typical of physics-based simulations.
  • Dynamic Programming Solvers - Includes solvers for Markov Decision Processes using value and policy iteration through dynamic programming.
  • Agent Performance Evaluators - Implements tools for assessing agent behavior and policy stability by calculating average and peak returns over thousands of episodes.
  • Markov Decision Process Solvers - Implements algorithms for calculating optimal action sequences in environments where future states depend on current actions.
  • Monte Carlo Sampling Methods - Implements techniques for estimating state values by averaging accumulated rewards from complete interaction episodes.
  • Monte Carlo Trajectory Estimators - Implements Monte Carlo methods to estimate expected returns by averaging cumulative rewards from complete episodes.
  • Cross-Entropy Method Implementations - Implements an optimization approach for policies based on high-reward trajectories to solve continuous control environments.
  • Parallel Experience Collection - Provides the capability to execute multiple identical simulation instances simultaneously to accelerate training data gathering.
  • Reinforcement Learning Trading Frameworks - Provides a framework for training agents that use reinforcement learning to analyze market data and execute asset trading.
  • Reinforcement Learning Training - Provides general frameworks for training agents to solve control problems within virtual scenarios and simulations.
  • Multi-Agent Training - Implements architectures for coordinating multiple specialized agents to cooperate or compete within shared environments.
  • Navigation Agent Training - Includes implementations for teaching virtual agents to move through spatial environments using perception data.
  • Learning and Reference - Deep RL Nanodegree repo.

    • Star history

    Star history chart for udacity/deep-reinforcement-learningStar history chart for udacity/deep-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 udacity/deep-reinforcement-learning do?

    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.

    What are the main features of udacity/deep-reinforcement-learning?

    The main features of udacity/deep-reinforcement-learning are: RL Agent Implementation Frameworks, Reinforcement Learning Curricula, Actor-Critic Architectures, Deterministic Policy Gradients, Deep Q-Learning Implementations, DDPG Implementations, Policy Gradient Implementations, Policy Gradient Optimizers.

    Which projects share features with udacity/deep-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.… dennybritz/reinforcement-learning — This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous… packtpublishing/deep-reinforcement-learning-hands-on — This project serves as an educational resource and training framework for developing intelligent agents through deep… andri27-ts/reinforcement-learning — This project is a collection of reinforcement learning implementations and educational materials written in Python. It… openai/baselines — Baselines is a comprehensive suite of frameworks for reinforcement learning algorithm implementation, imitation… morvanzhou/tutorials — This repository is a comprehensive collection of instructional guides and practical examples for Python development,…

    Projects sharing features with Deep Reinforcement Learning

    These projects share indexed features with Deep 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

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    • dennybritz/reinforcement-learningdennybritz avatar

      dennybritz/reinforcement-learning

      22,039View on GitHub↗

      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 approxima

      Jupyter Notebook
      View on GitHub↗22,039
  • 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
  • andri27-ts/reinforcement-learningandri27-ts avatar

    andri27-ts/Reinforcement-Learning

    4,722View on GitHub↗

    This project is a collection of reinforcement learning implementations and educational materials written in Python. It provides neural network architectures for solving control tasks through deep reinforcement learning, spanning value-based and policy-gradient methods. The repository includes a library of evolutionary strategies and genetic algorithms as alternatives to gradient-based learning. It also features a model-based system for predicting future environment states and rewards to enable internal simulation and offline planning. The codebase covers a wide range of capabilities, includi

    Jupyter Notebooka2cartificial-intelligencedeep-learning
    View on GitHub↗4,722
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