Papers with Code 아카이브

3D Face Modelling

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코드

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

https://github.com/jakegrigsby/dmc_remastered

공식 코드

Measuring Visual Generalization in Continuous Control from Pixels

https://github.com/janscholten/ppmp

공식 코드

Deep Reinforcement Learning with Feedback-based Exploration

https://github.com/jaysparrow/rl-for-active-mapping

공식 코드

Learning Continuous Control Policies for Information-Theoretic Active Perception

https://github.com/jdchang1/milo

공식 코드

Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage

https://github.com/jhoon-cho/mbtl

공식 코드

Model-Based Transfer Learning for Contextual Reinforcement Learning

https://github.com/jigangkim/autoreset_rl

공식 코드

Automating Reinforcement Learning with Example-based Resets

https://github.com/jihwan-jeong/cbop

공식 코드

Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization

https://github.com/jincate/ccep

공식 코드

Centralized Cooperative Exploration Policy for Continuous Control Tasks

https://github.com/jinzhuluo/euclideanda

공식 코드

Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control

https://github.com/joelouismarino/variational_rl

공식 코드

Iterative Amortized Policy Optimization

https://github.com/johannesack/offlinerlstructurednonstationarity

공식 코드

Offline Reinforcement Learning from Datasets with Structured Non-Stationarity

https://github.com/jqueeney/gpi

공식 코드

Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse

https://github.com/jqueeney/robust-safe-rl

공식 코드

Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees

https://github.com/jren03/garage

공식 코드

Hybrid Inverse Reinforcement Learning

https://github.com/junfanlin/continuous-transition

공식 코드

Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp

https://github.com/kacperkan/conerf

공식 코드

CoNeRF: Controllable Neural Radiance Fields

https://github.com/kallekku/cmcgs

공식 코드

Continuous Monte Carlo Graph Search

https://github.com/keraJLi/synthetic-gymnax

공식 코드

Discovering Minimal Reinforcement Learning Environments

https://github.com/kevinhuang8/cem-gd

공식 코드

CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning

https://github.com/kindredresearch/SenseAct

공식 코드

Benchmarking Reinforcement Learning Algorithms on Real-World Robots

https://github.com/kindredresearch/arp

공식 코드

Autoregressive Policies for Continuous Control Deep Reinforcement Learning

https://github.com/kngwyu/infomax-option-critic

공식 코드

Learning Diverse Options via InfoMax Termination Critic

https://github.com/konwook/mticl

공식 코드

Learning Shared Safety Constraints from Multi-task Demonstrations

https://github.com/koulanurag/dream-and-search

공식 코드

Dream and Search to Control: Latent Space Planning for Continuous Control

https://github.com/kwanyun/LeGO_code

공식 코드

LeGO: Leveraging a Surface Deformation Network for Animatable Stylized Face Generation with One Example

https://github.com/lava-lab/dynabs

공식 코드

Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions

https://github.com/lava-lab/fba

공식 코드

Correct-by-construction reach-avoid control of partially observable linear stochastic systems

https://github.com/layer6ai-labs/CAE

공식 코드

C-Learning: Horizon-Aware Cumulative Accessibility Estimation

https://github.com/lemutisme/dr-sac

공식 코드

DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty

https://github.com/lil-lab/drif

공식 코드

Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight

https://github.com/ling-pan/SD3

공식 코드

Softmax Deep Double Deterministic Policy Gradients

https://github.com/lionelblonde/liayn-pytorch

공식 코드

Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning

https://github.com/lionelblonde/sam-tf

공식 코드

Sample-Efficient Imitation Learning via Generative Adversarial Nets

https://github.com/liuf1990/3DFC

공식 코드

3D Face Modeling From Diverse Raw Scan Data

https://github.com/liyheng/GPO

공식 코드

Guided Policy Optimization under Partial Observability

https://github.com/liyichen1998/bootstrapdagger-mftplp

공식 코드

Agnostic Interactive Imitation Learning: New Theory and Practical Algorithms

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