https://github.com/shiningsunnyday/mcts-chess
공식 코드Alpha-Mini: Minichess Agent with Deep 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/shiwj16/SSINet
공식 코드Self-Supervised Discovering of Interpretable Features for Reinforcement Learning
https://github.com/shiwj16/raa-drl
공식 코드Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
https://github.com/shlee94/Off2OnRL
공식 코드Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
https://github.com/shreyasc-13/star
공식 코드Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation
https://github.com/shreyasc-13/suno
공식 코드Distributional Off-Policy Evaluation for Slate Recommendations
https://github.com/shreyassr123/double-sor-q-learning
공식 코드Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning
https://github.com/shripaddeshmukh/xrl_with_trajectories
공식 코드https://github.com/shromonag/adversarial_testing
공식 코드Verifying Controllers Against Adversarial Examples with Bayesian Optimization
https://github.com/shuhewang1998/reinforcement-learning-enhanced-llms-a-survey
공식 코드https://github.com/shukla-yash/lsts-icaps-24
공식 코드Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
https://github.com/shunyo/predicting-optimal-value-functions
공식 코드https://github.com/shutech2001/DOLCE
공식 코드DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current Effects
https://github.com/shuyueL/FFNet
공식 코드https://github.com/shuzhaoxie/armol
공식 코드Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach
https://github.com/shwetanlp/CHQ-Summ
공식 코드Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards
https://github.com/si0wang/pdml
공식 코드Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
https://github.com/si0wang/vicrit
공식 코드ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs
https://github.com/siavash91/TC-DQN
공식 코드Adaptive Traffic Control with Deep Reinforcement Learning:Towards State-of-the-art and Beyond
https://github.com/sical/drlviz
공식 코드DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
https://github.com/siddharthverma314/chai-naacl-2022
공식 코드CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning
https://github.com/sidharth0094/game-of-ur-reinforcement-learning
공식 코드https://github.com/siemens/industrialbenchmark
공식 코드Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
https://github.com/siemens/policy_search_bb-alpha
공식 코드Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
https://github.com/siemens/powergym
공식 코드PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems
https://github.com/sihengli99/repo
공식 코드https://github.com/sildater/parangonar
공식 코드Online Symbolic Music Alignment with Offline Reinforcement Learning
https://github.com/silihuang-ai/idt
공식 코드In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-Thought
https://github.com/simonzhan-code/step-wise_saferl_pixel
공식 코드State-Wise Safe Reinforcement Learning With Pixel Observations
https://github.com/simula-complex/deepqtest
공식 코드https://github.com/sinannasir/Power-Control-asilomar
공식 코드Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks
https://github.com/sinannasir/Spectrum-Power-Allocation
공식 코드Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks
https://github.com/sinatayebati/vlm-uncertainty
공식 코드https://github.com/sino-huang/brittleness_of_lrs
공식 코드https://github.com/sisl/AutomotivePOMDPs.jl
공식 코드Decomposition Methods with Deep Corrections for Reinforcement Learning
https://github.com/sisl/AutomotiveSafeRL
공식 코드Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.