Papers with Code 아카이브

3D Face Modelling

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

https://github.com/boschresearch/ube-mbrl

공식 코드

Model-Based Uncertainty in Value Functions

https://github.com/caelan/SS-Replan

공식 코드

Online Replanning in Belief Space for Partially Observable Task and Motion Problems

https://github.com/calvincbzhang/orso

공식 코드

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization

https://github.com/camall3n/markov-state-abstractions

공식 코드

Learning Markov State Abstractions for Deep Reinforcement Learning

https://github.com/causalml/bcrl

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Learning Bellman Complete Representations for Offline Policy Evaluation

https://github.com/cccedric/cpql

공식 코드

Boosting Continuous Control with Consistency Policy

https://github.com/ceisenach/MPG

공식 코드

Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications

https://github.com/chakrabortyde/mujoco-control-tasks

공식 코드

Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environment

https://github.com/changchencc/plandq

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PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer

https://github.com/changmin-yu/ccwm_code

공식 코드

Learning State Representations via Retracing in Reinforcement Learning

https://github.com/choyingw/M3-LRN

공식 코드

Accurate 3D Facial Geometry Prediction by Multi-Task, Multi-Modal, and Multi-Representation Landmark Refinement Network

https://github.com/choyingw/SynergyNet

공식 코드

Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

https://github.com/cleardusk/3DDFA_V2

공식 코드

Towards Fast, Accurate and Stable 3D Dense Face Alignment

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

공식 코드

Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction

https://github.com/cloud0723/offline-mlirl

공식 코드

When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

https://github.com/combra-lab/pop-spiking-deep-rl

공식 코드

Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control

https://github.com/conglu1997/v-d4rl

공식 코드

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

https://github.com/contactrika/bulb

공식 코드

Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control

https://github.com/core-robotics-lab/icct

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Learning Interpretable, High-Performing Policies for Autonomous Driving

https://github.com/cosynus-lix/STAR

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Reconciling Spatial and Temporal Abstractions for Goal Representation

https://github.com/cx441000319/drc

공식 코드

The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards

https://github.com/danielpalen/value_expansion

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Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning

https://github.com/danijar/dreamer

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Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/dee0512/Temporally-Layered-Architecture

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Optimizing Attention and Cognitive Control Costs Using Temporally-Layered Architectures

https://github.com/deepmind/dm_control

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DeepMind Control Suite

https://github.com/deepmind/spriteworld

공식 코드

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

https://github.com/denisyarats/drq

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Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

https://github.com/denisyarats/proto

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Reinforcement Learning with Prototypical Representations

https://github.com/desikrengarajan/fedora

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Federated Ensemble-Directed Offline Reinforcement Learning

https://github.com/dhruvms/DenseTrafficEval

공식 코드

Driving in Dense Traffic with Model-Free Reinforcement Learning

https://github.com/dhruvms/HighwayTraffic

공식 코드

Driving in Dense Traffic with Model-Free Reinforcement Learning

https://github.com/dig-beihang/ami

공식 코드

Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence

https://github.com/dkangin/baselines

공식 코드

On-Policy Trust Region Policy Optimisation with Replay Buffers

https://github.com/dlpbc/nm-metarl

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Context Meta-Reinforcement Learning via Neuromodulation

https://github.com/dmksjfl/DARC

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Efficient Continuous Control with Double Actors and Regularized Critics

https://github.com/dmksjfl/GD3

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Value Activation for Bias Alleviation: Generalized-activated Deep Double Deterministic Policy Gradients

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