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/tum-pbs/diffusion-based-flow-prediction
공식 코드https://github.com/tum-pbs/pbdl-book
공식 코드https://github.com/tum-pbs/pbdl-dataset
공식 코드https://github.com/tumbais/hybridmadrl-amod
공식 코드Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
https://github.com/tumbais/structured-rl
공식 코드Structured Reinforcement Learning for Combinatorial Decision-Making
https://github.com/tumcps/commonpower
공식 코드CommonPower: A Framework for Safe Data-Driven Smart Grid Control
https://github.com/tumcps/pytupli
공식 코드PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning Projects
https://github.com/tung-nd/cwbc
공식 코드Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning
https://github.com/tunglm2203/erlvlm
공식 코드https://github.com/tunglm2203/pcdt
공식 코드https://github.com/tunglm2203/tirl
공식 코드On the Perturbed States for Transformed Input-robust Reinforcement Learning
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/f-IRL
공식 코드f-IRL: Inverse Reinforcement Learning via State Marginal Matching
https://github.com/twni2016/memory-rl
공식 코드When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment
https://github.com/twni2016/pomdp-baselines
공식 코드When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment
https://github.com/twni2016/self-predictive-rl
공식 코드Bridging State and History Representations: Understanding Self-Predictive RL
https://github.com/two2tee/WorldModelPlanning
공식 코드https://github.com/tyler-ingebrand/functionencoderrl
공식 코드https://github.com/typoverflow/wiserl
공식 코드Hindsight Preference Learning for Offline Preference-based Reinforcement Learning
https://github.com/tyq1024/rlx2
공식 코드RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
https://github.com/uber-common/deep-neuroevolution
공식 코드VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
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/uber-research/go-explore
공식 코드https://github.com/uber-research/normative-uncertainty
공식 코드https://github.com/uber-research/plato-research-dialogue-system
공식 코드Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
https://github.com/uber-research/poet
공식 코드https://github.com/ubisoft/directbehaviorspecification
공식 코드Direct Behavior Specification via Constrained Reinforcement Learning
https://github.com/ubisoft/ubisoft-la-forge-ASAF
공식 코드Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
https://github.com/ucamrl/eqs
공식 코드https://github.com/ucamrl/xrlflow
공식 코드X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation
https://github.com/ucb-bar/autophase
공식 코드AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
https://github.com/ucbtns/dai
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.