Showing 7 open source projects for "8-puzzle reinforcement learning python"

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  • 1
    Evolutionary Algorithm

    Evolutionary Algorithm

    Evolutionary Algorithm using Python

    ...Users can explore basic genetic algorithm setups, match phrase examples, pathfinding challenges, and microbial GA variants, as well as evolution strategy approaches like NES. The project also links classical evolutionary approaches with neural networks, illustrating how evolution can be used for model training in reinforcement learning and supervised contexts.
    Downloads: 0 This Week
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  • 2
    Reinforcement-learning

    Reinforcement-learning

    Implementation of Reinforcement Learning Algorithms. Python, OpenAI

    Reinforcement-learning is a widely used educational repository that provides implementations, exercises, and solutions for a broad range of reinforcement learning algorithms, designed to complement foundational texts and courses in the field. The project collects popular approaches such as dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, deep Q-networks, and policy gradient techniques, often demonstrated with Python and OpenAI Gym environments so users can experiment with agents learning in simulated tasks. ...
    Downloads: 0 This Week
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  • 3
    Gym

    Gym

    Toolkit for developing and comparing reinforcement learning algorithms

    Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no...
    Downloads: 5 This Week
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  • 4
    AlphaTensor

    AlphaTensor

    AI discovers faster, efficient algorithms for matrix multiplication

    AlphaTensor, developed by Google DeepMind, is the research codebase accompanying the 2022 Nature publication “Discovering faster matrix multiplication algorithms with reinforcement learning.” The project demonstrates how reinforcement learning can be used to automatically discover efficient algorithms for matrix multiplication — a fundamental operation in computer science and numerical computation. The repository is organized into four main components: algorithms, benchmarking, nonequivalence, and recombination. ...
    Downloads: 0 This Week
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  • 5
    MADDPG

    MADDPG

    Code for the MADDPG algorithm from a paper

    MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in...
    Downloads: 0 This Week
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  • 6
    Baselines

    Baselines

    High-quality implementations of reinforcement learning algorithms

    Unlike the other two, openai/baselines is not currently a maintained or prominent repo in the OpenAI organization (and I found no strong reference in OpenAI’s main GitHub). Historically, “baselines” repositories are often used for baseline implementations of reinforcement learning algorithms or reference models (e.g. in the RL domain). If there was an OpenAI “baselines” repo, it might have contained reference implementations for reinforcement learning or model policy baselines to compare new...
    Downloads: 0 This Week
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  • 7
    Coach

    Coach

    Enables easy experimentation with state of the art algorithms

    ...Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. Coach supports a large number of environments which can be solved using reinforcement learning.
    Downloads: 0 This Week
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