Papers with Code 아카이브

Reinforcement Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

162

보존된 평가 기록

1

코드

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

https://github.com/tristandeleu/pytorch-maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/tristandeleu/pytorch-maml-rl

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/trojai/trojai

The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models

https://github.com/tsinghua-rll/gym-gridworld

OpenAI Gym

https://github.com/tsurumeso/chainer-grad-cam

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

https://github.com/ttajmajer/morl-dv

Modular Multi-Objective Deep Reinforcement Learning with Decision Values

https://github.com/ttengwang/ESGN

Streamlined Dense Video Captioning

https://github.com/tuanpnm99/RLPongAgent

Proximal Policy Optimization Algorithms

https://github.com/tuantle/simple_nn_with_numpy

Searching for Activation Functions

https://github.com/tuantran23012000/Recommendation-system

Deep Reinforcement Learning for List-wise Recommendations

https://github.com/tuomaso/SC2LE-implementation

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/tushar-agarwal2909/Bayes_by_Backprop-TF

Weight Uncertainty in Neural Networks

https://github.com/ty4b112/pytorch_MINERVA

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

https://github.com/typoverflow/OfflineRL-Lib

AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

https://github.com/tyunist/memory_efficient_mish_swish

Searching for Activation Functions

https://github.com/uber-archive/plato-research-dialogue-system

Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning

https://github.com/uber-research/GTN

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

https://github.com/uber-research/ape-x

Distributed Prioritized Experience Replay

https://github.com/uber-research/backpropamine

Differentiable plasticity: training plastic neural networks with backpropagation

https://github.com/uber-research/differentiable-plasticity

Differentiable plasticity: training plastic neural networks with backpropagation

https://github.com/uber-research/ga-world-models

Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/uber/fiber

Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods

https://github.com/ucl-exoplanets/ADC2023-baseline

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/ugo-nama-kun/DQN-chainer

Playing Atari with Deep Reinforcement Learning

https://github.com/uicdice/imm-language-modeling

Induced Model Matching: How Restricted Models Can Help Larger Ones

https://github.com/uicdice/imm-reinforce

Induced Model Matching: How Restricted Models Can Help Larger Ones

https://github.com/uidmice/placement-rl

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

https://github.com/ukachyuthan/Rewards_in_RL

Reward Engineering for Object Pick and Place Training

https://github.com/ukyh/switch_disc_caption

Self-critical Sequence Training for Image Captioning

https://github.com/ulissigroup/uncertainty_benchmarking

Accurate Uncertainties for Deep Learning Using Calibrated Regression

https://github.com/ulstu/ml

Recurrent Models of Visual Attention

https://github.com/ulstu/robotics_ml

Recurrent Models of Visual Attention

https://github.com/umd-huang-lab/reinforcement-learning-via-spectral-methods

Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning

https://github.com/umersheikh846/WQMIX

Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning

https://github.com/uncharted-technologies/risk-and-uncertainty

Estimating Risk and Uncertainty in Deep Reinforcement Learning

https://github.com/universebh/text_generation_fsa_gan

Long Text Generation via Adversarial Training with Leaked Information

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