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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/jhu-lcsr/good_robot

공식 코드

"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer

https://github.com/jianda-chen/ambs

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Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities

https://github.com/jianlin-cheng/DRLComplex

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DRLComplex: Reconstruction of protein quaternary structures using deep reinforcement learning

https://github.com/jiawei-ren/diffmimic

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DiffMimic: Efficient Motion Mimicking with Differentiable Physics

https://github.com/jiaweihhuang/Steering_Markovian_Agents

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Learning to Steer Markovian Agents under Model Uncertainty

https://github.com/jiaweihhuang/confounded-pomdp-exp

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A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes

https://github.com/jiaweihhuang/heuristic_mebp

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Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL

https://github.com/jiaweihhuang/tiered-rl-experiments

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Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret

https://github.com/jibril-frej/jcrec

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Course Recommender Systems Need to Consider the Job Market

https://github.com/jiefeng-cse/analytical-lyapunov-function-discovery

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Analytical Lyapunov Function Discovery: An RL-based Generative Approach

https://github.com/jiefeng-cse/stable-ddpg-for-voltage-control

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Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control

https://github.com/jigangkim/autoreset_rl

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Automating Reinforcement Learning with Example-based Resets

https://github.com/jihwan-jeong/cbop

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Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization

https://github.com/jihyeonseong/DTF-net

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Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning

https://github.com/jincan333/lot

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Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate

https://github.com/jingliang-duan/dsac-t

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Distributional Soft Actor-Critic with Three Refinements

https://github.com/jingliang-duan/dsac-v2

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Distributional Soft Actor-Critic with Three Refinements

https://github.com/jingwu6/lm_ag

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The New Agronomists: Language Models are Experts in Crop Management

https://github.com/jingyue2000/in-context_learning_for_automated_driving

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Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language Models

https://github.com/jinonoel/blottorl

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Reinforcement Learning Agents in Colonel Blotto

https://github.com/jinyangjiangai/quantile-based-policy-optimization

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Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient Algorithms

https://github.com/jinyansu1/a-dlp

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Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive Rewards

https://github.com/jinzhuluo/euclideanda

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Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control

https://github.com/jity16/when-to-update-your-model-constrained-model-based-reinforcement-learning

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When to Update Your Model: Constrained Model-based Reinforcement Learning

https://github.com/jiuzhouh/reward-engineering-for-generating-seg

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Reward Engineering for Generating Semi-structured Explanation

https://github.com/jiwanchung/esper

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Multimodal Knowledge Alignment with Reinforcement Learning

https://github.com/jiwonjeon9603/maser

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MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer

https://github.com/jizhi-zhang/billp

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Large Language Models are Learnable Planners for Long-Term Recommendation

https://github.com/jjbong/strangeness_exploration

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Strangeness-driven Exploration in Multi-Agent Reinforcement Learning

https://github.com/jjgarau/DAGPolicyGradient

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Policy Gradient RL Algorithms as Directed Acyclic Graphs

https://github.com/jjjerome/mbt_gym

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Model-based gym environments for limit order book trading

https://github.com/jkulhanek/a2cat-vn-pytorch

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Vision-based Navigation Using Deep Reinforcement Learning

https://github.com/jkulhanek/dmlab-vn

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Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning

https://github.com/jkulhanek/robot-visual-navigation

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Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning

https://github.com/jlin816/rewards-from-language

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Inferring Rewards from Language in Context

https://github.com/jlubars/RL-MPC-LaneMerging

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Combining Reinforcement Learning with Model Predictive Control for On-Ramp Merging

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