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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/huage001/painttransformer

공식 코드

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction

https://github.com/huanghanchi/camrl

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Curriculum-based Asymmetric Multi-task Reinforcement Learning

https://github.com/huangjuite/radar-navigation

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Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions

https://github.com/huangwl18/modular-rl

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One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

https://github.com/huanzhang12/ATLA_robust_RL

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Robust Reinforcement Learning on State Observations with Learned Optimal Adversary

https://github.com/huawei-noah/HEBO/tree/master/NAP

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End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes

https://github.com/huawei-noah/HEBO/tree/master/PMDB

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Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief

https://github.com/huawei-noah/SMARTS

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SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

https://github.com/huawei-noah/hebo

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Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation

https://github.com/huawei-noah/noah-research

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Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis

https://github.com/huawei-noah/trustworthyAI

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Causal Discovery with Reinforcement Learning

https://github.com/huawei-noah/vega

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An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization

https://github.com/hubbs5/or-gym

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OR-Gym: A Reinforcement Learning Library for Operations Research Problems

https://github.com/huckiyang/interconnect-routing-gym

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Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

https://github.com/huda-lab/rl-epidemic-benchmark

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Planning Multiple Epidemic Interventions with Reinforcement Learning

https://github.com/huggingface/jat

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Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent

https://github.com/hugochan/RL-based-Graph2Seq-for-NQG

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Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation

https://github.com/huihuichyan/think-j

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Think-J: Learning to Think for Generative LLM-as-a-Judge

https://github.com/human0being/uvip-rl

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UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms

https://github.com/humanmllm/humanomniv2

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HumanOmniV2: From Understanding to Omni-Modal Reasoning with Context

https://github.com/humemai/agent-room-env-v0

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A Machine With Human-Like Memory Systems

https://github.com/humemai/agent-room-env-v1

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A Machine with Short-Term, Episodic, and Semantic Memory Systems

https://github.com/humemai/agent-room-env-v2-lstm

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Leveraging Knowledge Graph-Based Human-Like Memory Systems to Solve Partially Observable Markov Decision Processes

https://github.com/humphreymunn/modularityneat

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Towards Understanding the Link Between Modularity and Performance in Neural Networks for Reinforcement Learning

https://github.com/hupihe/rsd4

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Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning

https://github.com/hustvl/alphadrive

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AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning

https://github.com/hwang-ua/inac_pytorch

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The In-Sample Softmax for Offline Reinforcement Learning

https://github.com/hwj20/apex_exp

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APEX: Empowering LLMs with Physics-Based Task Planning for Real-time Insight

https://github.com/hybridsystemslab/obstacleavoidancehyrl

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Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control Policies via Hybrid Control

https://github.com/hybridsystemslab/unitcirclehyrl

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Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control Policies via Hybrid Control

https://github.com/hychen-naza/SSA-RL

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Safe and Sample-efficient Reinforcement Learning for Clustered Dynamic Environments

https://github.com/hyeonahkimm/delta_cs

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Improved Off-policy Reinforcement Learning in Biological Sequence Design

https://github.com/hyperplane-lab/RLAfford

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End-to-End Affordance Learning for Robotic Manipulation

https://github.com/hysonlab/arcroute

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Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing Problems

https://github.com/hyunin-lee/TempoRL

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Tempo Adaptation in Non-stationary Reinforcement Learning

https://github.com/hzaskywalker/PlasticineLab

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PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics

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