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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/dylan-goetting/Newsvendor-RL

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Newsvendor Model with Deep Reinforcement Learning

https://github.com/dylanashley/catastrophic-forgetting

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Does the Adam Optimizer Exacerbate Catastrophic Forgetting?

https://github.com/dylanashley/reward-weighted-regression

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Reward-Weighted Regression Converges to a Global Optimum

https://github.com/dyne-submission/dynamics-aware-embeddings

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Dynamics-aware Embeddings

https://github.com/easeonway/explainable-causal-reinforcement-learning

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Explainable Reinforcement Learning via a Causal World Model

https://github.com/eastonhuch/rome

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RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions

https://github.com/echalmers/modulated_td_error

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Reinforcement Learning with Brain-Inspired Modulation can Improve Adaptation to Environmental Changes

https://github.com/eclipsess/awesome-efficient-reasoning-llms

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Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

https://github.com/ed2-paper/ed2

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Continuous Control With Ensemble Deep Deterministic Policy Gradients

https://github.com/edauce/ijcnn-cca

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Concurrent Credit Assignment for Data-efficient Reinforcement Learning

https://github.com/edbeeching/3d_control_deep_rl

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Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer

https://github.com/edbeeching/godot_rl_agents

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Godot Reinforcement Learning Agents

https://github.com/eddycmu/demystify-long-cot

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Demystifying Long Chain-of-Thought Reasoning in LLMs

https://github.com/edlanglois/mamdp

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How RL Agents Behave When Their Actions Are Modified

https://github.com/edward-sun/easy-to-hard

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Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision

https://github.com/egg-west/mcep

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Mildly Constrained Evaluation Policy for Offline Reinforcement Learning

https://github.com/egojr/optagan

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OptAGAN: Entropy-based finetuning on text VAE-GAN

https://github.com/ehsansabouni/cdc2024_rl_adpative_mpc_cbf

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Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems

https://github.com/eilamshapira/humanchoiceprediction

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Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation

https://github.com/eivindeb/rluav

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Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments

https://github.com/ejcatt/aixijs_mentee

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Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal Agent

https://github.com/ejmejm/discrete-representations-for-continual-rl

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Harnessing Discrete Representations For Continual Reinforcement Learning

https://github.com/eladsar/rbi

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Constrained Policy Improvement for Safe and Efficient Reinforcement Learning

https://github.com/elasriz/PHIHP

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Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning

https://github.com/electronicarts/character-motion-vaes

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Character Controllers Using Motion VAEs

https://github.com/elementai/avenue

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Real-Time Reinforcement Learning

https://github.com/eleninisioti/neuroevolution_in_transfer_learning

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When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?

https://github.com/eleurent/highway-env

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Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees

https://github.com/eleurent/rl-agents

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Budgeted Reinforcement Learning in Continuous State Space

https://github.com/elicassion/StARformer

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StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning

https://github.com/elisejiuqizhang/RLMSAD

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Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection

https://github.com/elliottyan/luffy

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Learning to Reason under Off-Policy Guidance

https://github.com/eloialonso/iris

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Transformers are Sample-Efficient World Models

https://github.com/elvisyjlin/lf2gym

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Deep Reinforcement Learning for Playing 2.5D Fighting Games

https://github.com/emanuel-metzenthin/rl-scene-text-detection

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Weakly Supervised Scene Text Detection using Deep Reinforcement Learning

https://github.com/embodiedcity/embodied-r.code

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Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning

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