Papers with Code 아카이브

Reinforcement Learning

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보존된 평가 기록

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코드

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

https://github.com/zhaoolee/garss

Offline Reinforcement Learning for LLM Multi-Step Reasoning

https://github.com/zhengant/dqn_reversi

Deep Reinforcement Learning with Double Q-learning

https://github.com/zhengsr3/Reinforcement_Learning_Pointer_Networks_TSP_Pytorch

Neural Combinatorial Optimization with Reinforcement Learning

https://github.com/zhipeixu/fakeshield

AvatarShield: Visual Reinforcement Learning for Human-Centric Video Forgery Detection

https://github.com/zhixuan-lin/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/zhou-henry/distributed-distributional-drq

Distributed Distributional Deterministic Policy Gradients

https://github.com/zhoujianxing123/minigo_v17

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/zhouzypaul/wsrl

Efficient Online Reinforcement Learning with Offline Data

https://github.com/zhyack/UDRG

A Semi-Supervised Approach for Low-Resourced Text Generation

https://github.com/ziangqin-stu/impl_data-effiient-hrl

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/ziangqin-stu/impl_hiro

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/ziangqin-stu/rl_hiro

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/zijianh4/CROP-leaderboard.github.io

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

https://github.com/zilongzheng/visdial-gnn

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

https://github.com/zilunpeng/svrg_for_policy_evaluation_with_fewer_gradients

SVRG for Policy Evaluation with Fewer Gradient Evaluations

https://github.com/ziqiao-shang/aunce

Representation Learning with Contrastive Predictive Coding

https://github.com/zivaharoni/capacity-rl

Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning

https://github.com/ziyaoli/molgan-without-mode-collapse

MolGAN: An implicit generative model for small molecular graphs

https://github.com/ziyizhang27/sdpo

REBEL: Reinforcement Learning via Regressing Relative Rewards

https://github.com/zjlab-ammi/llm4rl

Proximal Policy Optimization Algorithms

https://github.com/zlr20/saferl_kit

Safe Exploration in Continuous Action Spaces

https://github.com/zoeyuchao/MPE-pytorch

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/zoeyuchao/MPEnew-pytorch

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/zoeyuchao/mappo

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

https://github.com/zoli333/Weight-Normalization

Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

https://github.com/zpschang/seqGAN

Adversarial Learning for Neural Dialogue Generation

https://github.com/zsz-hst/RL_single_chase_point

Proximal Policy Optimization Algorithms

https://github.com/zubair-irshad/imitation_learning

OpenAI Gym

https://github.com/zuoxingdong/VIN_PyTorch_Visdom

Value Iteration Networks

https://github.com/zuoym15/craves.ai

CRAVES: Controlling Robotic Arm with a Vision-based Economic System

https://github.com/zxtan98/cprocgen

Leveraging Procedural Generation to Benchmark Reinforcement Learning

https://github.com/zynk13/dueling-dqn-Reinforcement-learning

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/zzl-pointcloud/Data_Augmentation_Zoo_for_Object_Detection

GridMask Data Augmentation

https://github.com/zzmtsvv/ORL

Mildly Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/zzmtsvv/ad-gta

In-context Reinforcement Learning with Algorithm Distillation

https://github.com/zzmtsvv/rl_task

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

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