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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/jangirrishabh/Overcoming-exploration-from-demos

Overcoming Exploration in Reinforcement Learning with Demonstrations

https://github.com/jastfkjg/DDPG_Torcs_PyTorch

Simulated Car Racing Championship: Competition Software Manual

https://github.com/jcwleo/curiosity-driven-exploration-pytorch

Proximal Policy Optimization Algorithms

https://github.com/jcwleo/random-network-distillation-pytorch

Proximal Policy Optimization Algorithms

https://github.com/jemaw/gym-safety

Lyapunov-based Safe Policy Optimization for Continuous Control

https://github.com/jesbu1/carl

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

https://github.com/jfpettit/flare

Proximal Policy Optimization Algorithms

https://github.com/jhare96/reinforcement-learning

Proximal Policy Optimization Algorithms

https://github.com/jingliang-duan/dsac-t

Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors

https://github.com/jongornet14/HyperController

Proximal Policy Optimization Algorithms

https://github.com/jqueeney/geppo

Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse

https://github.com/jrobine/actor-critic

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/jsztompka/MultiAgent-PPO

Proximal Policy Optimization Algorithms

https://github.com/jsztompka/PPO-demo

Proximal Policy Optimization Algorithms

https://github.com/jturner65/Getup-DartEnv

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

https://github.com/juliusfrost/Research-Paper-Implementations

Proximal Policy Optimization Algorithms

https://github.com/juliusfrost/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/juliuskunze/cwvae-jax

Learning Latent Dynamics for Planning from Pixels

https://github.com/junming-yang/mopo

MOPO: Model-based Offline Policy Optimization

https://github.com/justinjfu/diagnosing_qlearning

Diagnosing Bottlenecks in Deep Q-learning Algorithms

https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution

Proximal Policy Optimization Algorithms

https://github.com/jw1401/PPO-Tensorflow-2.0

Proximal Policy Optimization Algorithms

https://github.com/jyqhahah/rl_maddpg_matd3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/kairproject/kair_algorithms_draft

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

https://github.com/kaixindelele/DRLib

Overcoming Exploration in Reinforcement Learning with Demonstrations

https://github.com/kalifou/robotics-rl-srl

Simple random search provides a competitive approach to reinforcement learning

https://github.com/karim-abdel/fact

Explaining RL Decisions with Trajectories

https://github.com/kavosh8/MAC

Mean Actor Critic

https://github.com/kayuksel/pytorch-ars

Simple random search provides a competitive approach to reinforcement learning

https://github.com/kc-ml2/SimpleDreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/kdally/fault-tolerant-flight-control-drl

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

https://github.com/khansel01/nes-npg

Towards Generalization and Simplicity in Continuous Control

https://github.com/kiminh/dual-policy-distillation

Dual Policy Distillation

https://github.com/krasing/DRLearningCollaboration

Continuous control with deep reinforcement learning

https://github.com/krasing/DRLearningContinuousControl

Continuous control with deep reinforcement learning

https://github.com/ku2482/discor.pytorch

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

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