Papers with Code 아카이브

Reinforcement Learning

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코드

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

https://github.com/AshrithSagar/option-critic

The Option-Critic Architecture

https://github.com/AurelianTactics/bcq_tensorflow

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/AurelianTactics/dqfd-with-keras

Deep Q-learning from Demonstrations

https://github.com/AurelianTactics/dqnclipped_dqnreg_prelim_implementation

Evolving Reinforcement Learning Algorithms

https://github.com/AutoML-4Paradigm/ERAS

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/AutoML-4Paradigm/NSCaching

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/AutoML-4Paradigm/S2S

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/AutoML-Research/S2S

NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding

https://github.com/AutumnWu/Streamlined-Off-Policy-Learning

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

https://github.com/AvivNavon/AuxiLearn

Adapting Auxiliary Losses Using Gradient Similarity

https://github.com/AyanKumarBhunia/on-the-fly-FGSBIR

Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval

https://github.com/B-C-WANG/ReinforcementLearningInAutoPilot

World Models

https://github.com/BBDrive/Meta-SGD-RL

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

https://github.com/BBDrive/TESP

Meta Reinforcement Learning with Task Embedding and Shared Policy

https://github.com/BBloggsbott/k-armed-bandits

A Tutorial on Thompson Sampling

https://github.com/BCHoagland/DARLA

DARLA: Improving Zero-Shot Transfer in Reinforcement Learning

https://github.com/BCHoagland/VINS

Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling

https://github.com/BFAnas/BranchingDQN_keras

Action Branching Architectures for Deep Reinforcement Learning

https://github.com/BH4/Deep-Reinforcement-Learning

Playing Atari with Deep Reinforcement Learning

https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/COPGAgent.py

Clipped-Objective Policy Gradients for Pessimistic Policy Optimization

https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/PPOAgent.py

Proximal Policy Optimization Algorithms

https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/TD3Agent.py

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/BPYap/Question-Generation

Ask the Right Questions: Active Question Reformulation with Reinforcement Learning

https://github.com/BPrasad123/Data_Science_Learning

APES: a Python toolbox for simulating reinforcement learning environments

https://github.com/BY571/CQL

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/BY571/DQN-Atari-Agents

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/BY571/FQF-and-Extensions

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

https://github.com/BY571/IQN

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/BY571/Implicit-Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/BY571/MBPO

When to Trust Your Model: Model-Based Policy Optimization

https://github.com/BY571/Munchausen-RL

Munchausen Reinforcement Learning

https://github.com/BY571/Normalized-Advantage-Function-NAF-

Continuous Deep Q-Learning with Model-based Acceleration

https://github.com/BY571/SAC_discrete

Soft Actor-Critic for Discrete Action Settings

https://github.com/BY571/Soft-Actor-Critic-and-Extensions

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

https://github.com/BY571/Upside-Down-Reinforcement-Learning

Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions

https://github.com/Baichenjia/BHER

Hindsight Experience Replay

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