https://github.com/salesforce/ai-economist
공식 코드The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
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/salesforce/coderl
공식 코드CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
https://github.com/salesforce/strategically-robust-ai
공식 코드Learning to Play General-Sum Games Against Multiple Boundedly Rational Agents
https://github.com/salesforce/warp-drive
공식 코드WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
https://github.com/salmakh1/ACING
공식 코드ACING: Actor-Critic for Instruction Learning in Black-Box Large Language Models
https://github.com/samholt/neurallaplacecontrol
공식 코드https://github.com/samindaa/RLLib
공식 코드https://github.com/sampo-kuutti/training-adversarial-agents
공식 코드Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies
https://github.com/sampo-kuutti/weakly-supervised-rl-highway-driving
공식 코드Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
https://github.com/samsungsailmontreal/stgg-al
공식 코드Generating $π$-Functional Molecules Using STGG+ with Active Learning
https://github.com/samuela/e-stops
공식 코드Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
https://github.com/samuelmallick/mpcrl-greenhouse
공식 코드Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control
https://github.com/samuki/reinforce-joey
공식 코드Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation
https://github.com/sangdon/PAC-confidence-set
공식 코드PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
https://github.com/sangwkim/RL_manufacturing
공식 코드Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement Learning
https://github.com/saodem74/Transfer-Learning-in-Reinforcement-Learning
공식 코드Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
https://github.com/sarah-keren/rlpe
공식 코드https://github.com/sarapohland/stranger-danger
공식 코드https://github.com/sarmueller/gibo
공식 코드https://github.com/sarodyatawatta/hintrl
공식 코드Hint assisted reinforcement learning: an application in radio astronomy
https://github.com/sastpg/covo
공식 코드Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning
https://github.com/satchitchatterji/shieldedmarlthesis
공식 코드Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement Learning
https://github.com/satori-reasoning/satori-swe
공식 코드Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering
https://github.com/scale-lab/DRiLLS
공식 코드https://github.com/schafert/inverse-irl-guppy
공식 코드Bayesian Inverse Reinforcement Learning for Collective Animal Movement
https://github.com/schatty/EMAC
공식 코드https://github.com/schatty/mocco
공식 코드Guided Exploration in Reinforcement Learning via Monte Carlo Critic Optimization
https://github.com/schmidtdominik/lapo
공식 코드https://github.com/schroederdewitt/mackrl
공식 코드https://github.com/schuderer/bprl
공식 코드Sim-Env: Decoupling OpenAI Gym Environments from Simulation Models
https://github.com/scil-vital/tracktolearn
공식 코드https://github.com/scil-vital/tractoracle-irt
공식 코드Exploring the robustness of TractOracle methods in RL-based tractography
https://github.com/scil-vital/tractoraclenet
공식 코드TractOracle: towards an anatomically-informed reward function for RL-based tractography
https://github.com/scottfleming/rl-missingness
공식 코드https://github.com/scslabisu/aggym
공식 코드AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning
https://github.com/sdicastro/KOVA
공식 코드Kalman meets Bellman: Improving Policy Evaluation through Value Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.