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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/dyne-submission/dynamics-aware-embeddings

공식 코드

Dynamics-aware Embeddings

https://github.com/ed2-paper/ed2

공식 코드

Continuous Control With Ensemble Deep Deterministic Policy Gradients

https://github.com/ed2-source-code/ed2

공식 코드

ED2: Environment Dynamics Decomposition World Models for Continuous Control

https://github.com/electronicarts/character-motion-vaes

공식 코드

Character Controllers Using Motion VAEs

https://github.com/elementai/avenue

공식 코드

Real-Time Reinforcement Learning

https://github.com/eleurent/highway-env

공식 코드

Understanding and Mitigating the Limitations of Prioritized Experience Replay

https://github.com/ermongroup/MetaIRL

공식 코드

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

https://github.com/existentialrobotics/rl_active_multi_target_tracking

공식 코드

Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories

https://github.com/fabiopardo/tonic

공식 코드

Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking

https://github.com/facebookresearch/WhereDidMyOptimumGo

공식 코드

Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods

https://github.com/facebookresearch/dcd

공식 코드

Stabilizing Unsupervised Environment Design with a Learned Adversary

https://github.com/facebookresearch/dcem

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The Differentiable Cross-Entropy Method

https://github.com/facebookresearch/drqv2

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Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/facebookresearch/go-fresh

공식 코드

Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping

https://github.com/facebookresearch/hsd3

공식 코드

Hierarchical Skills for Efficient Exploration

https://github.com/facebookresearch/mtm

공식 코드

Masked Trajectory Models for Prediction, Representation, and Control

https://github.com/facebookresearch/myosuite

공식 코드

MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control

https://github.com/facebookresearch/svg

공식 코드

On the model-based stochastic value gradient for continuous reinforcement learning

https://github.com/farama-foundation/arcade-learning-environment

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CALE: Continuous Arcade Learning Environment

https://github.com/ff93/parameter-based-value-functions

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Parameter-Based Value Functions

https://github.com/fiorenza2/OffCon3

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OffCon$^3$: What is state of the art anyway?

https://github.com/flairox/behaviour-distillation

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Behaviour Distillation

https://github.com/frankzheng2022/prise

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PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control

https://github.com/frankzheng2022/taco

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TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning

https://github.com/frt03/generalized_dt

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Generalized Decision Transformer for Offline Hindsight Information Matching

https://github.com/frt03/mxt_bench

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A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation

https://github.com/frt03/pic

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Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

https://github.com/garyz712/drlpiddronecontrol

공식 코드

AirPilot: Interpretable PPO-based DRL Auto-Tuned Nonlinear PID Drone Controller for Robust Autonomous Flights

https://github.com/geraudnt/rosarl

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ROSARL: Reward-Only Safe Reinforcement Learning

https://github.com/geraudnt/skill_machines

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Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning

https://github.com/gfnorg/diffusion-finetuning

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Amortizing intractable inference in diffusion models for vision, language, and control

https://github.com/giarcieri/assessing-the-influence-of-models-on-the-performance-of-reinforcement-learning-algorithms

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Which Model to Trust: Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms for Continuous Control Tasks

https://github.com/gkswamy98/fast_irl

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Inverse Reinforcement Learning without Reinforcement Learning

https://github.com/gkswamy98/replay_est

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Minimax Optimal Online Imitation Learning via Replay Estimation

https://github.com/gkswamy98/sequence_model_il

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Sequence Model Imitation Learning with Unobserved Contexts

https://github.com/google-deepmind/dmc_vision_benchmark

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DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors

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