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Reinforcement Learning (RL)

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하위 태스크: 6
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보존된 평가 기록: 3

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/YuhangSong/SGAN

공식 코드

Learning Approximate Stochastic Transition Models

https://github.com/YunqiuXu/SHA-KG

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Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games

https://github.com/Yusufma03/CVRL

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Contrastive Variational Reinforcement Learning for Complex Observations

https://github.com/Yusufma03/DPFRL

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Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations

https://github.com/YyzHarry/SV-RL

공식 코드

Harnessing Structures for Value-Based Planning and Reinforcement Learning

https://github.com/Z-T-WANG/ConvergentDQN

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Convergent and Efficient Deep Q Network Algorithm

https://github.com/Z-T-WANG/DeepReinforcementLearningControlOfQuantumCartpoles

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Deep Reinforcement Learning Control of Quantum Cartpoles

https://github.com/ZJULearning/DMP

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Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference

https://github.com/ZONG0004/MacroHFT

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MacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading

https://github.com/Zanette-Labs/efficient-reasoning

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Training Language Models to Reason Efficiently

https://github.com/Zealoter/ERRL

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ELO-Rated Sequence Rewards: Advancing Reinforcement Learning Models

https://github.com/ZehaoJin/Reinforcement-Learning-2019

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Learning Principle of Least Action with Reinforcement Learning

https://github.com/ZhengyaoJiang/GTG

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Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning

https://github.com/ZhengyaoJiang/NLRL

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Neural Logic Reinforcement Learning

https://github.com/ZhengyaoJiang/PGPortfolio

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Cryptocurrency Portfolio Management with Deep Reinforcement Learning

https://github.com/ZhengyaoJiang/graphbackup

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Graph Backup: Data Efficient Backup Exploiting Markovian Transitions

https://github.com/ZikangXiong/rl-detect-and-denoise-defense

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Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising

https://github.com/ZiyuanMa/DHC

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Distributed Heuristic Multi-Agent Path Finding with Communication

https://github.com/ZmeiGorynych/generative_playground

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Grammars and reinforcement learning for molecule optimization

https://github.com/aabl-lab/ioda_roman_2024

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Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies

https://github.com/aair-lab/chirp

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Autonomous Option Invention for Continual Hierarchical Reinforcement Learning and Planning

https://github.com/aaronsnoswell/unimodal-irl

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Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms

https://github.com/abdulhaim/basis-irl

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Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience

https://github.com/abhi-iyer/meta-learning

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Collision Avoidance Robotics Via Meta-Learning (CARML)

https://github.com/abhi460729/GATN

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Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains

https://github.com/abhiksingla/UAV_obstacle_avoidance_controller

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Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledge

https://github.com/ablustrund/apps_plus

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StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback

https://github.com/abman23/autobs

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AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network Twins

https://github.com/absdnd/aux_distill

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Reinforcement Learning via Auxiliary Task Distillation

https://github.com/abukharin3/heron

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Deep Reinforcement Learning from Hierarchical Preference Design

https://github.com/ac-93/braille_RL

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Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille Keyboard

https://github.com/ac-93/tactile_gym

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Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation

https://github.com/acastagn/EF-OnTL

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Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning

https://github.com/acellera/acegen-open

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ACEGEN: Reinforcement learning of generative chemical agents for drug discovery

https://github.com/acoache/RL-DynamicRobustRisk

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Robust Reinforcement Learning with Dynamic Distortion Risk Measures

https://github.com/acoache/rl-dynamicconvexrisk

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Reinforcement Learning with Dynamic Convex Risk Measures

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