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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/2groza/action-refined-temporal-difference

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Toward Causal-Aware RL: State-Wise Action-Refined Temporal Difference

https://github.com/2m-kotb/curiosity-cem

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Sample-efficient Real-time Planning with Curiosity Cross-Entropy Method and Contrastive Learning

https://github.com/ActionEmbedding/ActionEmbedding

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Learning Action-Transferable Policy with Action Embedding

https://github.com/Akella17/Deep-Bayesian-Quadrature-Policy-Optimization

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Deep Bayesian Quadrature Policy Optimization

https://github.com/Algolzw/SPAC-Deformable-Registration

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Stochastic Actor-Executor-Critic for Image-to-Image Translation

https://github.com/AutumnWu/Streamlined-Off-Policy-Learning

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Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling

https://github.com/Breakend/BayesianPolicyGradients

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Bayesian Policy Gradients via Alpha Divergence Dropout Inference

https://github.com/Breakend/OptionGAN

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OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning

https://github.com/Breakend/ReproducibilityInContinuousPolicyGradientMethods

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Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control

https://github.com/CPS-TUWien/racing_dreamer

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Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing

https://github.com/ChristosKap/multi_timescale_replay

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Continual Reinforcement Learning with Multi-Timescale Replay

https://github.com/Cmeo97/MAIF

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Multimodal VAE Active Inference Controller

https://github.com/Cranial-XIX/DualSMC

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DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

https://github.com/DLR-RM/stable-baselines3

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Smooth Exploration for Robotic Reinforcement Learning

https://github.com/DSC-SPIDAL/rankpredictor

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Rank Position Forecasting in Car Racing

https://github.com/DSobscure/cgi_drl_platform

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An Unsupervised Video Game Playstyle Metric via State Discretization

https://github.com/Data-Science-in-Mechanical-Engineering/cheq

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Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors

https://github.com/Div99/IQ-Learn

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IQ-Learn: Inverse soft-Q Learning for Imitation

https://github.com/FeMa42/soil-tdm

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Imitation Learning by State-Only Distribution Matching

https://github.com/GhadaSokar/Dynamic-Sparse-Training-for-Deep-Reinforcement-Learning

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Dynamic Sparse Training for Deep Reinforcement Learning

https://github.com/Gp1g/RFL

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

https://github.com/HJDQN/HJQ

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Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls

https://github.com/Hangz-nju-cuhk/Rotate-and-Render

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Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images

https://github.com/HorizonRobotics/alf

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TAAC: Temporally Abstract Actor-Critic for Continuous Control

https://github.com/HumanCompatibleAI/atari-irl

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Inverse reinforcement learning for video games

https://github.com/HybridRobotics/MPC-CBF

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Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function

https://github.com/IntelLabs/DNS

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DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning

https://github.com/IrisLi17/self-imitation-via-reduction

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Solving Compositional Reinforcement Learning Problems via Task Reduction

https://github.com/KamyarGh/rl_swiss

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A Divergence Minimization Perspective on Imitation Learning Methods

https://github.com/KyunghyunLee/aes-rl

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An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search

https://github.com/LAIRLAB/ARS-experiments

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Exploration in Action Space

https://github.com/LAIRLAB/contrasting_exploration_rl

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Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective

https://github.com/LinZichuan/AdMRL

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Model-based Adversarial Meta-Reinforcement Learning

https://github.com/LunjunZhang/world-model-as-a-graph

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World Model as a Graph: Learning Latent Landmarks for Planning

https://github.com/MLIUB/PURE

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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation

https://github.com/MishaLaskin/curl

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CURL: Contrastive Unsupervised Representations for Reinforcement Learning

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