https://github.com/csun001/cclf
공식 코드CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient 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/cszmli/Rethink-RL-Sup
공식 코드Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems
https://github.com/cszmli/dp-without-adv
공식 코드Guided Dialog Policy Learning without Adversarial Learning in the Loop
https://github.com/ctp314/tfporl
공식 코드https://github.com/cts198859/deeprl_network
공식 코드Multi-agent Reinforcement Learning for Networked System Control
https://github.com/cts198859/deeprl_signal_control
공식 코드Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control
https://github.com/cuayahuitl/SimpleDS
공식 코드Strategic Dialogue Management via Deep Reinforcement Learning
https://github.com/cugbbaiyun/hgcn-mix
공식 코드Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution
https://github.com/cuitianyu961030/6gan
공식 코드https://github.com/cure-lab/deeptpi
공식 코드DeepTPI: Test Point Insertion with Deep Reinforcement Learning
https://github.com/curiosai/graph_conjectures
공식 코드A Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement Learning
https://github.com/curry2736/rl-malloc
공식 코드https://github.com/cvas-ug/simple-reactive-nn
공식 코드On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning
https://github.com/cvc-lab/rse_rl
공식 코드Reinforcement Learning of Self Enhancing Camera Image and Signal Processing
https://github.com/cviaai/RL-DBS
공식 코드Reinforcement Learning Framework for Deep Brain Stimulation Study
https://github.com/cvlab-epfl/disk
공식 코드https://github.com/cvlab-stonybrook/Scanpath_Prediction
공식 코드Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning
https://github.com/cwfparsonson/retro_branching
공식 코드Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories
https://github.com/cwibault/sorel_torel
공식 코드https://github.com/cx441000319/drc
공식 코드The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards
https://github.com/cxy1997/Transferable-Active-Grasping
공식 코드https://github.com/cyanrain7/trust-region-policy-optimisation-in-multi-agent-reinforcement-learning
공식 코드Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
https://github.com/cycraig/MP-DQN
공식 코드Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces
https://github.com/cyrusneary/verifiable-compositional-rl
공식 코드https://github.com/czp16/cde-offline-rl
공식 코드Learning from Sparse Offline Datasets via Conservative Density Estimation
https://github.com/czp16/fcsrl
공식 코드Feasibility Consistent Representation Learning for Safe Reinforcement Learning
https://github.com/czx-li/DP2O
공식 코드Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learning
https://github.com/d-tiapkin/max-entropy-exploration
공식 코드https://github.com/d-tiapkin/optimistic-psrl-experiments
공식 코드Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
https://github.com/d09942015ntu/leveraging_unlabeled_offline_rl
공식 코드https://github.com/d1024choi/traj-pred-irl
공식 코드https://github.com/da-molchanov/variance-networks
공식 코드Variance Networks: When Expectation Does Not Meet Your Expectations
https://github.com/daanklijn/neurocoevolution
공식 코드A coevolutionary approach to deep multi-agent reinforcement learning
https://github.com/daashuai/rl4evit
공식 코드Reinforcement Learning-based Token Pruning in Vision Transformers: A Markov Game Approach
https://github.com/dair-iitd/torpido
공식 코드https://github.com/danecor/VaST
공식 코드Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.