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/ilnura/lb_sgd
공식 코드Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
https://github.com/ilyaorson/cyberdreamcatcher
공식 코드An Attentive Graph Agent for Topology-Adaptive Cyber Defence
https://github.com/ilyasu123/rlntm
공식 코드https://github.com/imTurkey/Market-Making-with-Deep-Reinforcement-Learning-from-Limit-Order-Books
공식 코드Market Making with Deep Reinforcement Learning from Limit Order Books
https://github.com/imagry/aleph_star
공식 코드https://github.com/imanolarrieta/angrybirds
공식 코드https://github.com/imasmitja/rlforutracking
공식 코드https://github.com/imatge-upc/detection-2016-nipsws
공식 코드Hierarchical Object Detection with Deep Reinforcement Learning
https://github.com/imec-idlab/deep-sea-treasure
공식 코드A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark
https://github.com/imoneoi/RSP_JAX
공식 코드https://github.com/imoneoi/carla_env
공식 코드A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
https://github.com/imoneoi/onerl
공식 코드A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
https://github.com/improbable-ai/curiosity_redteam
공식 코드https://github.com/improbable-ai/eipo
공식 코드https://github.com/improbable-ai/harness-offline-rl
공식 코드Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting
https://github.com/inclusionai/areal
공식 코드AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning
https://github.com/indujohniisc/GSQL
공식 코드https://github.com/indylab/nxdo
공식 코드https://github.com/info-structures/ais
공식 코드https://github.com/ingambe/End2End-Job-Shop-Scheduling-CP
공식 코드https://github.com/ingambe/rl4semiconductorfabsched
공식 코드Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning
https://github.com/ingyn/macsrl
공식 코드Logic-based Reward Shaping for Multi-Agent Reinforcement Learning
https://github.com/innixma/dex
공식 코드Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning
https://github.com/inpefess/basic-rl-prover
공식 코드Project proposal: A modular reinforcement learning based automated theorem prover
https://github.com/insomnia94/FTMU
공식 코드Fast Template Matching and Update for Video Object Tracking and Segmentation
https://github.com/insomnia94/ISREG
공식 코드Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning
https://github.com/inspirai/wilderness-scavenger
공식 코드WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments
https://github.com/instadeepai/EGTA-NMARL
공식 코드https://github.com/instadeepai/Mava
공식 코드Mava: a research library for distributed multi-agent reinforcement learning in JAX
https://github.com/instadeepai/compass
공식 코드Combinatorial Optimization with Policy Adaptation using Latent Space Search
https://github.com/instadeepai/jumanji
공식 코드Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
https://github.com/instadeepai/memento
공식 코드Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization
https://github.com/instadeepai/og-marl
공식 코드Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning
https://github.com/instadeepai/outer-value-function-meta-rl
공식 코드Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function
https://github.com/instadeepai/qd-skill-discovery-benchmark
공식 코드Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.