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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/patrickhart/jaxdl

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/pauldaoudi/food

Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

https://github.com/pcchenxi/lapo-offlienrl

Latent-Variable Advantage-Weighted Policy Optimization for Offline RL

https://github.com/peisuke/awr

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

https://github.com/petsol/ContinuousControl_UnityAgent_DDPG_Udacity

Continuous control with deep reinforcement learning

https://github.com/pkasala/ContinuesControl

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/plaffa/discrete-world-models

Deep Neuroevolution of Recurrent and Discrete World Models

https://github.com/polixir/NeoRL

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/polixir/OfflineRL

MOPO: Model-based Offline Policy Optimization

https://github.com/prajwalgatti/DRL-Collaboration-and-Competition

Continuous control with deep reinforcement learning

https://github.com/prajwalgatti/DRL-Continuous-Control

Continuous control with deep reinforcement learning

https://github.com/pranz24/pytorch-soft-actor-critic

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/pytorch/rl/tree/main/examples/cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/pytorch/rl/tree/main/examples/ppo

Proximal Policy Optimization Algorithms

https://github.com/pytorch/rl/tree/main/examples/sac

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/pytorch/rl/tree/main/examples/td3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/qihongl/demo-random-search-RL

Simple random search provides a competitive approach to reinforcement learning

https://github.com/quantumiracle/Popular-RL-Algorithms

Memory-based control with recurrent neural networks

https://github.com/raja17021998/Augmented-Random-Search-Implementation

Simple random search provides a competitive approach to reinforcement learning

https://github.com/ramanans1/dm_control

DeepMind Control Suite

https://github.com/ran-weii/cleanil

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

https://github.com/ravi-lanka-4/CoPiEr

Co-training for Policy Learning

https://github.com/ray-project/ray/tree/master/rllib

Continuous control with deep reinforcement learning

https://github.com/rcheng805/RL-CBF

End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Tasks

https://github.com/rddy/ReQueST

Learning Human Objectives by Evaluating Hypothetical Behavior

https://github.com/rehamessameltagoury/Urban-Self-Driving-Car

End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception

https://github.com/rehoss/lbmpc_semimarkov

An Experimental Design Perspective on Model-Based Reinforcement Learning

https://github.com/reinforcement-learning-kr/lets-do-irl

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

https://github.com/reinforcement-learning-kr/pg_travel

Proximal Policy Optimization Algorithms

https://github.com/reiniscimurs/TD3_Separate_Action

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/rejuvyesh/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/richardrl/cartpole-request-for-research

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/rikluost/RL_DQN_Pong

Continuous control with deep reinforcement learning

https://github.com/rk1998/robot-sac

Continuous control with deep reinforcement learning

https://github.com/rll/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

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