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Reinforcement Learning (RL)

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하위 태스크: 6
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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/btnorman/First-Explore

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First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-Offs

https://github.com/bu-cyber-physical-systems-lab/swannfs

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Sim-Anchored Learning for On-the-Fly Adaptation

https://github.com/buaawgj/lope

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Preference-Guided Reinforcement Learning for Efficient Exploration

https://github.com/budi-kurniawan/d2d-spl

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Discrete-to-Deep Supervised Policy Learning

https://github.com/buds-lab/recommender-sys-for-buildings-textmining-review

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Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literature

https://github.com/budzianowski/multiwoz

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LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization

https://github.com/buptlxh/conformer-rlpatching

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Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid Dispatching

https://github.com/burakdmb/quadsim

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QuadSim: A Quadcopter Rotational Dynamics Simulation Framework For Reinforcement Learning Algorithms

https://github.com/burcukoglu/p4o-predictiveprocessingppo

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Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization

https://github.com/byminji/SLTtrack

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Towards Sequence-Level Training for Visual Tracking

https://github.com/bys0318/squire

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SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning

https://github.com/bytarnish/agile

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AGILE: A Novel Reinforcement Learning Framework of LLM Agents

https://github.com/c-a-schiller/var4arl

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Virtual Augmented Reality for Atari Reinforcement Learning

https://github.com/c0derzer0/RSPMN

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Recurrent Sum-Product-Max Networks for Decision Making in Perfectly-Observed Environments

https://github.com/cagatayyildiz/oderl

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Continuous-Time Model-Based Reinforcement Learning

https://github.com/cair/DeepRTS

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Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games

https://github.com/cair/TsetlinMachine

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Self-timed Reinforcement Learning using Tsetlin Machine

https://github.com/cair/deep-warehouse

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Towards Model-based Reinforcement Learning for Industry-near Environments

https://github.com/cair/rl

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CaiRL: A High-Performance Reinforcement Learning Environment Toolkit

https://github.com/callmespring/causalrl

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Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework

https://github.com/caltech-netlab/acnportal-experiments

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ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

https://github.com/calvincbzhang/orso

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ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization

https://github.com/camall3n/markov-state-abstractions

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Learning Markov State Abstractions for Deep Reinforcement Learning

https://github.com/cambel/robot-learning-cl-dr

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Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study

https://github.com/cameron-chen/flow-iar

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Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning

https://github.com/caotians1/BabyAIPlusPlus

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Zero-Shot Compositional Policy Learning via Language Grounding

https://github.com/cap-ntu/baconian-project

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Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning

https://github.com/caraj7/t2i-r1

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T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT

https://github.com/carljohanhoel/BayesianRLForAutonomousDriving

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Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation

https://github.com/carljohanhoel/EnsembleQuantileNetworks

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Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving

https://github.com/carlosferrazza/humanoid-bench

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TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot Control

https://github.com/carolinewang01/dm2

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DM$^2$: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching

https://github.com/carolinssc/offline-rl-amod

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Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning

https://github.com/carolinssc/offline-rl-for-amod

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Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning

https://github.com/carolius/Multi-Objective-Reinforcement-Learning-from-AI-Feedback

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Multi-objective Reinforcement learning from AI Feedback

https://github.com/cas-lab-munich/sigmarl

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SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning

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