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/rinuboney/fpac
공식 코드https://github.com/rinuboney/keyq
공식 코드https://github.com/ripl/maps
공식 코드https://github.com/risal-shefin/xsrl
공식 코드xSRL: Safety-Aware Explainable Reinforcement Learning -- Safety as a Product of Explainability
https://github.com/rishavb123/EMMA
공식 코드External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling
https://github.com/risilab/Learnable_MMF
공식 코드Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs
https://github.com/riveSunder/carle
공식 코드Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity
https://github.com/rizar/actor-critic-public
공식 코드https://github.com/rjagerman/tois2019-safe-exploration-algorithm
공식 코드https://github.com/rl-boxes/safe-rl
공식 코드CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
https://github.com/rl-ina/lemgorl
공식 코드Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control
https://github.com/rl-interpretation/understandingRL
공식 코드Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
https://github.com/rl-language/rlc
공식 코드Rulebook: bringing co-routines to reinforcement learning environments
https://github.com/rl-tools/rl-tools
공식 코드RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control
https://github.com/rl-with-tns/acten_code
공식 코드https://github.com/rlacombe/adsorbrl
공식 코드AdsorbRL: Deep Multi-Objective Reinforcement Learning for Inverse Catalysts Design
https://github.com/rlai-lab/relod
공식 코드Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers
https://github.com/rlai-lab/remote-onboard-agent
공식 코드Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote Computers
https://github.com/rle-foundation/rlexplore
공식 코드RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning
https://github.com/rlgraph/rlgraph
공식 코드RLgraph: Modular Computation Graphs for Deep Reinforcement Learning
https://github.com/rlhflow/minimal-rl
공식 코드A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce
https://github.com/rll-research/b-pref
공식 코드B-Pref: Benchmarking Preference-Based Reinforcement Learning
https://github.com/rll-research/bpref
공식 코드https://github.com/rll-research/sim2seg
공식 코드Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data
https://github.com/rll-research/teachable
공식 코드https://github.com/rll-research/url_benchmark
공식 코드https://github.com/rllab-snu/safe-distributional-actor-critic
공식 코드Trust Region-Based Safe Distributional Reinforcement Learning for Multiple Constraints
https://github.com/rllab-snu/stage-wise-cmorl
공식 코드https://github.com/rllab/rllab
공식 코드Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rlworkgroup/metaworld
공식 코드Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
https://github.com/rm-r1-uiuc/rm-r1
공식 코드https://github.com/rmsander/interreplay
공식 코드https://github.com/rmst/rlrd
공식 코드https://github.com/rmst/rtrl
공식 코드https://github.com/robbertreijnen/dr-alns
공식 코드Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.