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/open-mmlab/mmrazor

DARTS: Differentiable Architecture Search

https://github.com/openai/gym

OpenAI Gym

https://github.com/openai/maddpg

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

https://github.com/openai/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/openai/universe-starter-agent

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/openai/vime

VIME: Variational Information Maximizing Exploration

https://github.com/openai/weightnorm

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

https://github.com/openbotai/nl2sql

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/opendilab/DI-engine

Deep Reinforcement Learning with Double Q-learning

https://github.com/opendilab/DI-engine/blob/main/ding/entry/serial_entry_sqil.py

SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

https://github.com/opendilab/DI-engine/blob/main/ding/policy/acer.py

Sample Efficient Actor-Critic with Experience Replay

https://github.com/opendilab/DI-engine/blob/main/ding/policy/c51.py

A Distributional Perspective on Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/coma.py

Counterfactual Multi-Agent Policy Gradients

https://github.com/opendilab/DI-engine/blob/main/ding/policy/d4pg.py

Distributed Distributional Deterministic Policy Gradients

https://github.com/opendilab/DI-engine/blob/main/ding/policy/impala.py

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

https://github.com/opendilab/DI-engine/blob/main/ding/policy/iqn.py

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/pdqn.py

Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qmix.py

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qrdqn.py

Distributional Reinforcement Learning with Quantile Regression

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qtran.py

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/rainbow.py

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/sac.py

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

https://github.com/opendilab/DI-engine/blob/main/ding/policy/sql.py

Reinforcement Learning with Deep Energy-Based Policies

https://github.com/opendilab/DI-engine/blob/main/ding/policy/td3.py

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/opendilab/DI-engine/blob/main/ding/policy/td3_bc.py

A Minimalist Approach to Offline Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/gail_irl_model.py

Generative Adversarial Imitation Learning

https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/her_reward_model.py

Hindsight Experience Replay

https://github.com/opendilab/DI-engine/blob/main/ding/reward_model/rnd_reward_model.py

Exploration by Random Network Distillation

https://github.com/opendilab/LightZero

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

https://github.com/openreasoner/openr

OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models

https://github.com/openrlbenchmark/openrlbenchmark

Deep Reinforcement Learning at the Edge of the Statistical Precipice

https://github.com/opent03/LangevinDQN-torch

Langevin DQN

https://github.com/opent03/neural_cars

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

https://github.com/opplieam/Pong-Deep-RL

Dueling Network Architectures for Deep Reinforcement Learning

https://github.com/oppsitre/RLift

Reinforcement Learning for Uplift Modeling

https://github.com/optimization-and-machine-learning-lab/open_spiel

OpenSpiel: A Framework for Reinforcement Learning in Games

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