Papers with Code 아카이브
Reinforcement Learning (RL)
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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/teslacool/m-curl
공식 코드Masked Contrastive Representation Learning for Reinforcement Learning
https://github.com/tessavdheiden/SCR
공식 코드Social Navigation with Human Empowerment driven Deep Reinforcement Learning
https://github.com/tessavdheiden/social_empowerment
공식 코드Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment
https://github.com/tesslerc/ActionRobustRL
공식 코드Action Robust Reinforcement Learning and Applications in Continuous Control
https://github.com/test-time-interaction/tti
공식 코드Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
https://github.com/tgGuo15/PriorImageCaption
공식 코드Improving Reinforcement Learning Based Image Captioning with Natural Language Prior
https://github.com/tgangwani/GuidanceRewards
공식 코드https://github.com/tgangwani/QDAgents
공식 코드Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity
https://github.com/thanard/me-trpo
공식 코드https://github.com/thanhnguyentang/drbqo
공식 코드On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
https://github.com/thanhnguyentang/mmdrl
공식 코드https://github.com/thanhnguyentang/neural-offline-rl
공식 코드VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function Approximation
https://github.com/thanosvlo/MARL-for-Anatomical-Landmark-Detection
공식 코드Multiple Landmark Detection using Multi-Agent Reinforcement Learning
https://github.com/theilem/uavSim
공식 코드UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning
https://github.com/theovincent/pbo
공식 코드https://github.com/thephet/SciTransformer
공식 코드Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning
https://github.com/theroadqaq/relift
공식 코드Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
https://github.com/thesmartrobot/ambigym
공식 코드A learning gap between neuroscience and reinforcement learning
https://github.com/thestephencasper/lm_white_box_attacks
공식 코드Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents
https://github.com/thestephencasper/white_box_rarl
공식 코드Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents
https://github.com/thibauttheate/risk-sensitive-policy-with-distributional-reinforcement-learning
공식 코드Risk-Sensitive Policy with Distributional Reinforcement Learning
https://github.com/thinklab-sjtu/eda-ai
공식 코드On Joint Learning for Solving Placement and Routing in Chip Design
https://github.com/thinklab-sjtu/rgm
공식 코드Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
https://github.com/thomasmiconi/meta-task-generator
공식 코드https://github.com/thomasrochefortb/sogym
공식 코드https://github.com/thu-bpm/gradlre
공식 코드Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
https://github.com/thu-coai/implicit-toxicity
공식 코드https://github.com/thu-keg/rearag
공식 코드https://github.com/thu-ml/CEURL
공식 코드PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
https://github.com/thu-ml/cep-energy-guided-diffusion
공식 코드https://github.com/thu-ml/tianshou
공식 코드Tianshou: a Highly Modularized Deep Reinforcement Learning Library
https://github.com/thu-rllab/lare
공식 코드Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning
https://github.com/thu-spmi/gus
공식 코드https://github.com/thudm/t1
공식 코드Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
https://github.com/thudm/treerl
공식 코드TreeRL: LLM Reinforcement Learning with On-Policy Tree Search
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