Papers with Code 아카이브

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/tensorpack/tensorpack/tree/master/examples/A3C-Gym

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/tensorpack/tensorpack/tree/master/examples/DeepQNetwork

Playing Atari with Deep Reinforcement Learning

https://github.com/tenvinc/CS4246-Project

Deep Q-learning from Demonstrations

https://github.com/teo-sl/DPLAN_pytorch

Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data

https://github.com/terne/dtuproject

Deep Reinforcement Learning for Programming Language Correction

https://github.com/terrierteam/pyterrier_rag

R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

https://github.com/tesatory/hsp

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

https://github.com/tfolkman/deep-learning-experiments

Deep Reinforcement Learning for Dialogue Generation

https://github.com/thaihungle/Memory-R

Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models

https://github.com/thainv0212/re-ddpg

Continuous control with deep reinforcement learning

https://github.com/thanhkaist/CCFDM1

Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model

https://github.com/theSparta/neural-symbolic-machines

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

https://github.com/theSparta/off_policy_mujoco

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/theamrzaki/text_summurization_abstractive_methods

A Deep Reinforced Model for Abstractive Summarization

https://github.com/thechrisyoon08/Multi-agent-reinforcement-learning

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

https://github.com/thechrisyoon08/marl

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

https://github.com/thejat/active-curricula-for-efficient-reinforcement-learning

Faster Reinforcement Learning Using Active Simulators

https://github.com/theneuralbeing/maml

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

https://github.com/theneuralbeing/neuroevolution

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

https://github.com/thlautenschlaeger/bpttv-lax

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

https://github.com/thomas-schillaci/SimPLe

Model-Based Reinforcement Learning for Atari

https://github.com/thomasaunger/babyai

https://github.com/thomashirtz/gym-hybrid

Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

https://github.com/thomashirtz/noisy-networks

Noisy Networks for Exploration

https://github.com/thomashirtz/pytorch-soft-actor-critic

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

https://github.com/thomashirtz/soft-actor-critic

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

https://github.com/thomashuang2017/simpson-dialogue-gan-master

Adversarial Learning for Neural Dialogue Generation

https://github.com/thomasscialom/summa-qa

Answers Unite! Unsupervised Metrics for Reinforced Summarization Models

https://github.com/thomasslloyd/EnvironmentMonitoring

Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning

https://github.com/thompsonj/nanoGCG

An Alternative Softmax Operator for Reinforcement Learning

https://github.com/threewisemonkeys-as/torched_impala

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

https://github.com/thudm/graphcad

Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

https://github.com/thudm/longwriter

LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning

https://github.com/thunil/Deep-Flow-Prediction

Physics-based Deep Learning

https://github.com/tiagoCuervo/CommonsGame

A multi-agent reinforcement learning model of common-pool resource appropriation

https://github.com/tianbaochou/CNAS

DARTS: Differentiable Architecture Search

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