https://github.com/deepseek-ai/deepseek-r1
공식 코드DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
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/deepseek-ai/deepseek-v2
공식 코드DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
https://github.com/deepsense-ai/Distributed-BA3C
공식 코드Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes
https://github.com/deepwlearning/deepwnetworks
공식 코드Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning
https://github.com/dematsunaga/alberdice
공식 코드https://github.com/demon0312/hcvrp_drl
공식 코드Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem
https://github.com/demosthen/activerl
공식 코드https://github.com/denisyarats/drq
공식 코드Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
https://github.com/denisyarats/exorl
공식 코드Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning
https://github.com/denisyarats/proto
공식 코드https://github.com/denisyarats/pytorch_sac_ae
공식 코드Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
https://github.com/dennybritz/reinforcement-learning
공식 코드https://github.com/derekmei233/MissLight
공식 코드Reinforcement Learning Approaches for Traffic Signal Control under Missing Data
https://github.com/derenlei/KG-RuleGuider
공식 코드Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning
https://github.com/des-lab/automata-learning-meets-shielding
공식 코드https://github.com/desikrengarajan/emrld
공식 코드Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments
https://github.com/desikrengarajan/fedora
공식 코드https://github.com/desikrengarajan/logo
공식 코드Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration
https://github.com/dfki-ric-quantum/qdrl-turtlebot-env
공식 코드Quantum Deep Reinforcement Learning for Robot Navigation Tasks
https://github.com/dfki-ric-quantum/qdrl-turtlebot-eval
공식 코드Quantum Deep Reinforcement Learning for Robot Navigation Tasks
https://github.com/dfki-ric-underactuated-lab/hopping_leg
공식 코드End-to-End Reinforcement Learning for Torque Based Variable Height Hopping
https://github.com/dhruvms/DenseTrafficEval
공식 코드Driving in Dense Traffic with Model-Free Reinforcement Learning
https://github.com/dhruvms/HighwayTraffic
공식 코드Driving in Dense Traffic with Model-Free Reinforcement Learning
https://github.com/diambra/arena
공식 코드DIAMBRA Arena: a New Reinforcement Learning Platform for Research and Experimentation
https://github.com/diandyu/trigger
공식 코드https://github.com/dibyaghosh/annotation_bootstrapping
공식 코드Visual Pre-Training on Unlabeled Images using Reinforcement Learning
https://github.com/dibyaghosh/dnc
공식 코드https://github.com/dibyaghosh/gcsl
공식 코드https://github.com/dido1998/CausalMBRL
공식 코드Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
https://github.com/diffmimic/diffmimic
공식 코드DiffMimic: Efficient Motion Mimicking with Differentiable Physics
https://github.com/dig-beihang/ami
공식 코드Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
https://github.com/digirl-agent/digiq
공식 코드Digi-Q: Learning Q-Value Functions for Training Device-Control Agents
https://github.com/digitalized-energy-systems/rl-opf-env-design
공식 코드https://github.com/dimichai/mo-tndp
공식 코드Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
https://github.com/dimikout3/GeneralExplorationPolicy
공식 코드https://github.com/dimikout3/MarsExplorer
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.