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3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/flavioschneider/ml_papers_presentations

Continuous control with deep reinforcement learning

https://github.com/floodsung/DDPG

Continuous control with deep reinforcement learning

https://github.com/flowersteam/curious

Continuous control with deep reinforcement learning

https://github.com/flowersteam/geppg

Parameter Space Noise for Exploration

https://github.com/flowersteam/rl_stats

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/fomorians-oss/awr

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

https://github.com/fomorians/forward-models

Recurrent Environment Simulators

https://github.com/fshamshirdar/pytorch-rdpg

Continuous control with deep reinforcement learning

https://github.com/fusion-ml/trajectory-information-rl

An Experimental Design Perspective on Model-Based Reinforcement Learning

https://github.com/gaetanserre/l2rpn-2022_ppo-baseline

Proximal Policy Optimization Algorithms

https://github.com/gaetanserre/lipo

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/garyzyr001/rethinking-airl

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/georgesung/TD3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/georgkruse/cleanqrl

Continuous control with deep reinforcement learning

https://github.com/ghliu/pytorch-ddpg

Continuous control with deep reinforcement learning

https://github.com/ghostFaceKillah/expert

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

https://github.com/gijskoning/ReproducingCURL

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/gmoss1/Kaggle-Halite-IV-RL

Proximal Policy Optimization Algorithms

https://github.com/goncharom/PPOv1

Proximal Policy Optimization Algorithms

https://github.com/google-deepmind/csil

IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/google-deepmind/spriteworld

COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration

https://github.com/google-research/dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/google/brain-tokyo-workshop/tree/master/WANNRelease

Weight Agnostic Neural Networks

https://github.com/google/evojax

Simple random search provides a competitive approach to reinforcement learning

https://github.com/google/trax

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

https://github.com/gowriaddepalli/ML_DL_Research_collab_base

Deep Reinforcement Learning framework for Autonomous Driving

https://github.com/gstoica27/cpg_ppo

Proximal Policy Optimization Algorithms

https://github.com/gtrll/dartenv

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

https://github.com/guillaumeboniface/reacher

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/gunjan99/Review-of-World-Models-using-Latex

World Models

https://github.com/guofeng201507/ISA-IPA-2020-05-05-IS1PT-GRP-High5-StockTrading

Proximal Policy Optimization Algorithms

https://github.com/gwbcho/dpo-replication

Distributional Policy Optimization: An Alternative Approach for Continuous Control

https://github.com/gwthomas/gtml

Continuous control with deep reinforcement learning

https://github.com/h-aboutalebi/SparceReward

Continuous control with deep reinforcement learning

https://github.com/halajadallah/RL-Quadcopter_project

Continuous control with deep reinforcement learning

https://github.com/hamidthri/navbot_ppo

Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

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