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Deep Reinforcement Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/trustworthy-ml-lab/robust_highutil_smoothed_drl
공식 코드Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents
https://github.com/tsinghua-fib-lab/road-planning-for-slums
공식 코드https://github.com/tumbais/gr-madrl-amod
공식 코드Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
https://github.com/tumbais/hybridmadrl-amod
공식 코드Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
https://github.com/tumbais/risksensitivesacforrobustdrlunderdistshifts
공식 코드Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts
https://github.com/tunglm2203/vq_robust_rl
공식 코드Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization
https://github.com/tuomaso/radial_rl
공식 코드https://github.com/tuomaso/radial_rl_v2
공식 코드https://github.com/twni2016/self-predictive-rl
공식 코드Bridging State and History Representations: Understanding Self-Predictive RL
https://github.com/tyq1024/rlx2
공식 코드RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
https://github.com/uber-research/atari-model-zoo
공식 코드An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents
https://github.com/uber-research/deep-neuroevolution
공식 코드https://github.com/ucb-bar/autophase
공식 코드AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
https://github.com/udeshmg/gta-rl
공식 코드Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning
https://github.com/ugr-sail/paper-drl_building
공식 코드An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control
https://github.com/ugurkanates/NeurIRS2019DroneChallengeRL
공식 코드Long-Term Planning with Deep Reinforcement Learning on Autonomous Drones
https://github.com/umd-huang-lab/wocar-rl
공식 코드Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
https://github.com/umrobotslang/does-drl-learn-to-navigate
공식 코드A Critical Investigation of Deep Reinforcement Learning for Navigation
https://github.com/uoe-agents/LIAM
공식 코드Agent Modelling under Partial Observability for Deep Reinforcement Learning
https://github.com/uoe-agents/epymarl
공식 코드Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/uoe-agents/lb-foraging
공식 코드Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/uoe-agents/robotic-warehouse
공식 코드Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/uoe-agents/seps
공식 코드Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
https://github.com/uzh-rpg/flightmare
공식 코드https://github.com/uzkent/EfficientObjectDetection
공식 코드Efficient Object Detection in Large Images using Deep Reinforcement Learning
https://github.com/valeoai/rainbow-iqn-apex
공식 코드Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field
https://github.com/varadvaidya/adapt-drones
공식 코드Dynamics-Invariant Quadrotor Control using Scale-Aware Deep Reinforcement Learning
https://github.com/veda-s4dhak/AQIL
공식 코드https://github.com/venktesh22/ExpressLanes_Deep-RL
공식 코드https://github.com/venturi123/drlinfluids
공식 코드https://github.com/victor-wang-902/synthetic-pretrain-rl
공식 코드Pre-training with Synthetic Data Helps Offline Reinforcement Learning
https://github.com/victorychain/Adversarial-Policy-Gradient-Augmentation
공식 코드Adversarial Policy Gradient for Deep Learning Image Augmentation
https://github.com/victorzxy/meowjong
공식 코드Building a 3-Player Mahjong AI using Deep Reinforcement Learning
https://github.com/viptankz/btr
공식 코드Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
https://github.com/vision-agh/drone_forest
공식 코드Tangled Program Graphs as an alternative to DRL-based control algorithms for UAVs
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