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Reinforcement Learning (RL)

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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

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

https://github.com/cocolico14/N-step-Dueling-DDQN-PER-Pacman

Deep Reinforcement Learning with Double Q-learning

https://github.com/code-cultivater/MAPPG

Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient

https://github.com/code-implementation1/Code1/tree/main/DecoMR

DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning

https://github.com/code-implementation1/Code5/tree/main/MAML

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/code-implementation1/Code6/tree/main/pnasnet

Progressive Neural Architecture Search

https://github.com/code-implementation1/Code9/tree/main/cflownets

CFlowNets: Continuous Control with Generative Flow Networks

https://github.com/codegoat24/unifiedreward

Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPO

https://github.com/codelion/optillm/tree/main/optillm/autothink

Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL

https://github.com/coderatwork7/attack

Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks

https://github.com/codeslord/RLforATCP

Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

https://github.com/cogitoergoread/rlcard3

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/cognitiveailab/textworldexpress

Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines

https://github.com/cognitivemodeling/finn

Weight Uncertainty in Neural Networks

https://github.com/col14m/cadrille

cadrille: Multi-modal CAD Reconstruction with Online Reinforcement Learning

https://github.com/colindbrown/columbia-deep-learning-project

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

https://github.com/colingwuyu/HedgingBox

Acme: A Research Framework for Distributed Reinforcement Learning

https://github.com/collinarnett/safex_trading_bot

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/collinarnett/xcalibra_trading_bot

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/columbia-robovision/decentralized-multiarm

Learning a Decentralized Multi-arm Motion Planner

https://github.com/consequentai/fneval

Let's Verify Step by Step

https://github.com/cool21th/ai_drug_discovery

ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?

https://github.com/cor3bit/bertsekas-marl

Multiagent Rollout Algorithms and Reinforcement Learning

https://github.com/core-robotics-lab/interpretable_ddts_aistats2020

Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning

https://github.com/coreylowman/ragz

Thinking Fast and Slow with Deep Learning and Tree Search

https://github.com/coreylowman/rl_simply

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/coreylowman/synthesis

Thinking Fast and Slow with Deep Learning and Tree Search

https://github.com/corl-team/CORL

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/cosmobobak/ttt-ml

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

https://github.com/cosmoquester/2021-dialogue-summary-competition

A Deep Reinforced Model for Abstractive Summarization

https://github.com/cosynus-lix/STAR

Data-Efficient Hierarchical Reinforcement Learning

https://github.com/cove9988/TradingGym

Deep Reinforcement Learning with Double Q-learning

https://github.com/cpark321/bayesian-neural-networks

Weight Uncertainty in Neural Networks

https://github.com/cpark321/uncertainty-deep-learning

Weight Uncertainty in Neural Networks

https://github.com/cpow-89/Augmented-Random-Search

Simple random search provides a competitive approach to reinforcement learning

https://github.com/cranial-xix/bome

BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach

https://github.com/cranial-xix/famo

Gradient Surgery for Multi-Task Learning

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