Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

2

보존된 평가 기록

3

코드

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

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

공식 코드

Benchmarks for Deep Off-Policy Evaluation

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

공식 코드

Unsupervised Learning of Object Structure and Dynamics from Videos

https://github.com/google-research/google-research/tree/master/pwil

공식 코드

Primal Wasserstein Imitation Learning

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

공식 코드

Predictive Information Accelerates Learning in RL

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

공식 코드

Learning Latent Dynamics for Planning from Pixels

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

공식 코드

An empirical investigation of the challenges of real-world reinforcement learning

https://github.com/gp1g/fedaa

공식 코드

FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning

https://github.com/gwthomas/safe-mbpo

공식 코드

Safe Reinforcement Learning by Imagining the Near Future

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

공식 코드

Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards

https://github.com/haarnoja/sac

공식 코드

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

https://github.com/han-adam/generalanalyze

공식 코드

Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System

https://github.com/hanglai/autombpo

공식 코드

On Effective Scheduling of Model-based Reinforcement Learning

https://github.com/hansenhua/ilid-offline-imitation-learning

공식 코드

How to Leverage Diverse Demonstrations in Offline Imitation Learning

https://github.com/hansenhua/ollie-offline-to-online-imitation-learning

공식 코드

OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning

https://github.com/harrylui98/zoac-tevc

공식 코드

Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems

https://github.com/hehongc/DCMRL

공식 코드

Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills

https://github.com/herolab-uga/bsac

공식 코드

Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement Learning

https://github.com/hmishfaq/lsac

공식 코드

Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning

https://github.com/hnyu/taac

공식 코드

TAAC: Temporally Abstract Actor-Critic for Continuous Control

https://github.com/holarissun/rewardshifting

공식 코드

Optimistic Curiosity Exploration and Conservative Exploitation with Linear Reward Shaping

https://github.com/huanzhang12/ATLA_robust_RL

공식 코드

Robust Reinforcement Learning on State Observations with Learned Optimal Adversary

https://github.com/icml2019-anonymous-author/Action-Robust-Reinforcement-Learning

공식 코드

Action Robust Reinforcement Learning and Applications in Continuous Control

https://github.com/idsia/gogepo

공식 코드

Goal-Conditioned Generators of Deep Policies

https://github.com/idsia/policyevaluator

공식 코드

General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States

https://github.com/ihuhuhu/RAC

공식 코드

Balancing Value Underestimation and Overestimation with Realistic Actor-Critic

https://github.com/iitkcpslab/rlstl

공식 코드

STL-Based Synthesis of Feedback Controllers Using Reinforcement Learning

https://github.com/indylab/delayeddreamer

공식 코드

Reinforcement Learning from Delayed Observations via World Models

https://github.com/intel-isl/LMRS

공식 코드

Learning to Guide Random Search

https://github.com/intelai/cerl

공식 코드

Collaborative Evolutionary Reinforcement Learning

https://github.com/intelligent-control-lab/absolute-policy-optimization

공식 코드

Absolute Policy Optimization

https://github.com/intelligent-reliable-autonomous-systems/gcrs-expert-abstractions

공식 코드

Learning with Expert Abstractions for Efficient Multi-Task Continuous Control

https://github.com/isluoshuang/macfn

공식 코드

Multi-Agent Continuous Control with Generative Flow Networks

https://github.com/jaayala/risk_aware_contextual_bandit

공식 코드

Risk-Aware Continuous Control with Neural Contextual Bandits

https://github.com/jacobandreas/psketch

공식 코드

Modular Multitask Reinforcement Learning with Policy Sketches

https://github.com/jacooba/hyper

공식 코드

SplAgger: Split Aggregation for Meta-Reinforcement Learning

https://github.com/jakegrigsby/deep_control

공식 코드

Towards Automatic Actor-Critic Solutions to Continuous Control

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