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3D Face Modelling

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

https://github.com/rl-tools/rl-tools

공식 코드

RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control

https://github.com/rll-research/url_benchmark

공식 코드

URLB: Unsupervised Reinforcement Learning Benchmark

https://github.com/rllab/rllab

공식 코드

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/rlr-smu/flow-pg

공식 코드

FlowPG: Action-constrained Policy Gradient with Normalizing Flows

https://github.com/rmsander/interreplay

공식 코드

Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks

https://github.com/rmst/rlrd

공식 코드

Reinforcement Learning with Random Delays

https://github.com/rmst/rtrl

공식 코드

Real-Time Reinforcement Learning

https://github.com/roberttlange/evosax

공식 코드

Discovering Evolution Strategies via Meta-Black-Box Optimization

https://github.com/robfiras/ls-iq

공식 코드

LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning

https://github.com/robintyh1/onpolicybaselines

공식 코드

Discretizing Continuous Action Space for On-Policy Optimization

https://github.com/roosephu/slbo

공식 코드

Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

https://github.com/rooshy-yang/becl

공식 코드

Behavior Contrastive Learning for Unsupervised Skill Discovery

https://github.com/ruihuang1967/Deep-GRAIL

공식 코드

Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR Streaming

https://github.com/ruohliuq/c-morl

공식 코드

C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front

https://github.com/saferl-lab/rlbenchnet

공식 코드

RLBenchNet: The Right Network for the Right Reinforcement Learning Task

https://github.com/sagar-pa/crystalbox

공식 코드

CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems

https://github.com/sahandrez/homomorphic_policy_gradient

공식 코드

Continuous MDP Homomorphisms and Homomorphic Policy Gradient

https://github.com/sahandrez/rotate_suite

공식 코드

Policy Gradient Methods in the Presence of Symmetries and State Abstractions

https://github.com/sail-sg/ild

공식 코드

Imitation Learning via Differentiable Physics

https://github.com/samuelmallick/supervised-learning-pwa-mpc

공식 코드

Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees

https://github.com/schatty/EMAC

공식 코드

Solving Continuous Control with Episodic Memory

https://github.com/schatty/mocco

공식 코드

Guided Exploration in Reinforcement Learning via Monte Carlo Critic Optimization

https://github.com/sebastianrisi/ga-world-models

공식 코드

Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/seongwoongcho/meta-controller

공식 코드

Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control

https://github.com/setarehc/deep_rl_regions

공식 코드

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning

https://github.com/sfujim/BCQ

공식 코드

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/sfujim/TD3

공식 코드

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/sfujim/td7

공식 코드

For SALE: State-Action Representation Learning for Deep Reinforcement Learning

https://github.com/shaneshixiang/rllabplusplus

공식 코드

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

https://github.com/shengjiewang-jason/efficientzerov2

공식 코드

EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data

https://github.com/shentao-yang/ampl_neurips2022

공식 코드

A Unified Framework for Alternating Offline Model Training and Policy Learning

https://github.com/shentao-yang/sdm-gan_icml2022

공식 코드

Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement Learning

https://github.com/shripaddeshmukh/xrl_with_trajectories

공식 코드

Explaining RL Decisions with Trajectories

https://github.com/shukla-yash/lsts-icaps-24

공식 코드

Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents

https://github.com/si0wang/pdml

공식 코드

Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy

https://github.com/siyuanlee/hess

공식 코드

Active Hierarchical Exploration with Stable Subgoal Representation Learning

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