https://github.com/ucd-dare/cardreamer
공식 코드CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving
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/ucl-dark/skillhack
공식 코드Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning
https://github.com/ucl-ift/noisyenv
공식 코드Simple Noisy Environment Augmentation for Reinforcement Learning
https://github.com/uclaml/gfa-rfe
공식 코드Uncertainty-Aware Reward-Free Exploration with General Function Approximation
https://github.com/ucuapps/modelicagym
공식 코드ModelicaGym: Applying Reinforcement Learning to Modelica Models
https://github.com/udeshmg/gta-rl
공식 코드Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning
https://github.com/ugr-sail/sinergym
공식 코드SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning
https://github.com/ugurkanates/NeurIRS2019DroneChallengeRL
공식 코드Long-Term Planning with Deep Reinforcement Learning on Autonomous Drones
https://github.com/ukyh/switch_disc_caption
공식 코드Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning
https://github.com/ulab-uiuc/iranker
공식 코드https://github.com/ulab-uiuc/router-r1
공식 코드Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning
https://github.com/ulab-uiuc/time-r1
공식 코드https://github.com/umass-foundation-model/hazard
공식 코드HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments
https://github.com/umass-ml4ed/feedback-gen-dpo
공식 코드Improving the Validity of Automatically Generated Feedback via Reinforcement Learning
https://github.com/umd-huang-lab/paad_adv_rl
공식 코드Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL
https://github.com/umd-huang-lab/protected
공식 코드Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies
https://github.com/umd-huang-lab/wocar-rl
공식 코드Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
https://github.com/umenberger/robust-exploration
공식 코드Robust exploration in linear quadratic reinforcement learning
https://github.com/umrobotslang/does-drl-learn-to-navigate
공식 코드A Critical Investigation of Deep Reinforcement Learning for Navigation
https://github.com/unc-optimization/ProxHSPGA
공식 코드A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
https://github.com/uncharted-technologies/robust-domain-randomization
공식 코드Robust Visual Domain Randomization for Reinforcement Learning
https://github.com/uni-medical/gmai-vl-r1
공식 코드GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning
https://github.com/unibwtas/collisionpro
공식 코드Collision Probability Distribution Estimation via Temporal Difference Learning
https://github.com/unstable-zeros/layered-ac
공식 코드Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic Learning
https://github.com/uoe-agents/LIAM
공식 코드Agent Modelling under Partial Observability for Deep Reinforcement Learning
https://github.com/uoe-agents/cmid
공식 코드Conditional Mutual Information for Disentangled Representations in Reinforcement Learning
https://github.com/uoe-agents/derl
공식 코드Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
https://github.com/uoe-agents/epymarl
공식 코드Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/uoe-agents/feedback-dt
공식 코드https://github.com/uoe-agents/hksl
공식 코드Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning
https://github.com/uoe-agents/imitation-learning-pcg
공식 코드Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments
https://github.com/uoe-agents/lb-foraging
공식 코드Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
https://github.com/uoe-agents/mate
공식 코드Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
https://github.com/uoe-agents/medoe
공식 코드Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition
https://github.com/uoe-agents/mvd
공식 코드Multi-view Disentanglement for Reinforcement Learning with Multiple Cameras
https://github.com/uoe-agents/robotic-warehouse
공식 코드Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.