Papers with Code 아카이브

Reinforcement Learning

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jojonki/hybrid-code-networks

Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

https://github.com/jojonki/pu-learning

Learning Classifiers on Positive and Unlabeled Data with Policy Gradient

https://github.com/jollyraven100/Quant_algorithmic-trading_and_More

Taming the Noise in Reinforcement Learning via Soft Updates

https://github.com/jollyraven100/Trade-Ideas-and-Research-Reference

Taming the Noise in Reinforcement Learning via Soft Updates

https://github.com/jonah-chen/alphazero-guerzhoy

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/jonahs99/baby-zero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/jonathan-laurent/AlphaZero.jl

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/jonaths/dqn-grid

Playing Atari with Deep Reinforcement Learning

https://github.com/jonaths/tf-dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/jondaa/CS236605FinalProject

Variance Networks: When Expectation Does Not Meet Your Expectations

https://github.com/joonleesky/train-procgen-pytorch

Leveraging Procedural Generation to Benchmark Reinforcement Learning

https://github.com/jordan-bird/synthetic-fruit-image-generator

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/josdyr/ml-agents

Unity: A General Platform for Intelligent Agents

https://github.com/joshiatul/game_playing

Playing Atari with Deep Reinforcement Learning

https://github.com/jozhang97/side-tuning

Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks

https://github.com/jparkerholder/ASEBO

From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization

https://github.com/jqhoogland/rgpy

Representation Learning with Contrastive Predictive Coding

https://github.com/jqueeney/geppo

Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse

https://github.com/jrobine/actor-critic

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/jsanderink/tue

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

https://github.com/jsbaan/DPAC-DialogueGAN

Adversarial Learning for Neural Dialogue Generation

https://github.com/jsztompka/DuelDQN

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/jsztompka/MultiAgent-PPO

Proximal Policy Optimization Algorithms

https://github.com/jsztompka/PPO-demo

Proximal Policy Optimization Algorithms

https://github.com/jturner65/Getup-DartEnv

OpenAI Gym

https://github.com/jugg1er/air

Value-Decomposition Networks For Cooperative Multi-Agent Learning

https://github.com/jukindle/aslaug3d_simulation

Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning

https://github.com/juliakreutzer/bandit-neuralmonkey

Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning

https://github.com/juliakreutzer/joeynmt

An Actor-Critic Algorithm for Sequence Prediction

https://github.com/juliusfrost/Research-Paper-Implementations

Proximal Policy Optimization Algorithms

https://github.com/juliusfrost/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/jundeli/quantum-gan

MolGAN: An implicit generative model for small molecular graphs

https://github.com/junmokane/offline-PEARL

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

https://github.com/junyoung0131/Mol-DQN

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/jurastm/differentiable_neural_plasticity

Differentiable plasticity: training plastic neural networks with backpropagation

https://github.com/justinchiu/NeuralDialog

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.