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Reinforcement Learning (RL)

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하위 태스크: 6
평가표: 2
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/acoache/rl-elicitabledynamicrisk

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Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning

https://github.com/act3-ace/corl

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CoRL: Environment Creation and Management Focused on System Integration

https://github.com/ada-research/combined_hpo_and_reward_shaping

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Combining Automated Optimisation of Hyperparameters and Reward Shape

https://github.com/adam-bjtu/autocoa

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Agent models: Internalizing Chain-of-Action Generation into Reasoning models

https://github.com/adam-bjtu/o1-coder

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o1-Coder: an o1 Replication for Coding

https://github.com/adanielnoel/Active-Inference-Capsule

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Online reinforcement learning with sparse rewards through an active inference capsule

https://github.com/adaptive-intelligent-robotics/qdax

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Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning

https://github.com/adaptive-rl/adarl-code

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AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

https://github.com/ademiadeniji/irm

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Skill-Based Reinforcement Learning with Intrinsic Reward Matching

https://github.com/ademiadeniji/lamp

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Language Reward Modulation for Pretraining Reinforcement Learning

https://github.com/adi3e08/physics_informed_model_based_rl

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Physics-Informed Model-Based Reinforcement Learning

https://github.com/adinlab/MOMBO

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Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning

https://github.com/adinlab/pac4sac

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PAC-Bayesian Soft Actor-Critic Learning

https://github.com/adipandas/gym_multirotor

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Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors

https://github.com/adishs/icml2020_rl-policy-teaching_code

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Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning

https://github.com/adishs/neurips2021_curriculum-teaching-demonstrations_code

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Curriculum Design for Teaching via Demonstrations: Theory and Applications

https://github.com/adishs/neurips2021_explicable-reward-design_code

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Reward Design for Reinforcement Learning Agents

https://github.com/adith387/slates_semisynth_expts

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Off-policy evaluation for slate recommendation

https://github.com/adityab/crossq

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CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity

https://github.com/admole/wind-rl

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Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control

https://github.com/adrialopezescoriza/demo3

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Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning

https://github.com/adrienbolland/jointly-learning-environments-and-control-policies-with-projected-stochastic-gradient-ascent

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Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascent

https://github.com/adwardlee/multitask-end-to-end-video-captioning

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End-to-End Video Captioning with Multitask Reinforcement Learning

https://github.com/aebeljs/verlpy

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VeRLPy: Python Library for Verification of Digital Designs with Reinforcement Learning

https://github.com/aegis1863/xjailbreak

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xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking

https://github.com/agakshat/spacefortress

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Learning Time-Sensitive Strategies in Space Fortress

https://github.com/agentification/language-integrated-vi

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How Can LLM Guide RL? A Value-Based Approach

https://github.com/agi-labs/continual_rl

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CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents

https://github.com/ago109/active-neural-generative-coding

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Backprop-Free Reinforcement Learning with Active Neural Generative Coding

https://github.com/ahjwang/messenger-emma

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Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning

https://github.com/ahmadnagib/SARL-RRM

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Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks

https://github.com/ahottung/EAS

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Efficient Active Search for Combinatorial Optimization Problems

https://github.com/ai-lab-science/deep-reinforcement-learning-for-mapless-navigation-in-intralogistics

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Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics

https://github.com/ai-secure/copa

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COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks

https://github.com/ai-secure/crop

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CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

https://github.com/ai-wand/concise-reasoning

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Concise Reasoning via Reinforcement Learning

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