https://github.com/dcgym/iroko
공식 코드Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control
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/ddaedalus/tres
공식 코드https://github.com/ddddwy/Reinforce-Paraphrase-Generation
공식 코드An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation
https://github.com/ddidacus/mol-moe
공식 코드Mol-MoE: Training Preference-Guided Routers for Molecule Generation
https://github.com/decisionforce/CoPO
공식 코드Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimization
https://github.com/decisionforce/EGPO
공식 코드https://github.com/decisionforce/metadrive
공식 코드MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning
https://github.com/declanoller/rwg-benchmark
공식 코드Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing
https://github.com/declare-lab/aste-rl
공식 코드Aspect Sentiment Triplet Extraction Using Reinforcement Learning
https://github.com/decodepfl/mad-rl-policy
공식 코드https://github.com/dee0512/Temporally-Layered-Architecture
공식 코드https://github.com/deepmind/active_ops
공식 코드https://github.com/deepmind/affordances_option_models
공식 코드What can I do here? A Theory of Affordances in Reinforcement Learning
https://github.com/deepmind/ai-safety-gridworlds
공식 코드https://github.com/deepmind/android_env
공식 코드https://github.com/deepmind/boxoban-levels
공식 코드https://github.com/deepmind/bsuite
공식 코드https://github.com/deepmind/constrained_optidice
공식 코드https://github.com/deepmind/deepmind-research
공식 코드Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/deepmind/deepmind-research/tree/master/physics_planning_games
공식 코드Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
https://github.com/deepmind/deepmind-research/tree/master/rl_unplugged
공식 코드RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
https://github.com/deepmind/diplomacy
공식 코드Learning to Play No-Press Diplomacy with Best Response Policy Iteration
https://github.com/deepmind/dm_alchemy
공식 코드Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
https://github.com/deepmind/dm_control
공식 코드https://github.com/deepmind/dm_fast_mapping
공식 코드Towards mental time travel: a hierarchical memory for reinforcement learning agents
https://github.com/deepmind/lab
공식 코드https://github.com/deepmind/lab2d
공식 코드https://github.com/deepmind/offpolicy_selection_eslb
공식 코드Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
https://github.com/deepmind/open_spiel
공식 코드A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
https://github.com/deepmind/pysc2
공식 코드https://github.com/deepmind/reverb
공식 코드https://github.com/deepmind/rgb_stacking
공식 코드Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
https://github.com/deepmind/scalable_agent
공식 코드IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
https://github.com/deepmind/spriteworld
공식 코드https://github.com/deepmind/streetlearn
공식 코드https://github.com/deepmind/zipfian_environments
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.