https://github.com/tairanhuang/cgar
공식 코드CGAR: Critic Guided Action Redistribution in Reinforcement Leaning
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/taivu1998/FlapAI-Bird
공식 코드FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques
https://github.com/tajwarfahim/proactive_interventions
공식 코드When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
https://github.com/takagi97/dissect-long-reason-models
공식 코드https://github.com/takuseno/d3rlpy
공식 코드https://github.com/takuyahiraoka/efficient-srgc-rl-with-a-high-rr-and-regularization
공식 코드https://github.com/takuyahiraoka/which-experiences-are-influential-for-rl-agents
공식 코드Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences
https://github.com/takuyahiraoka/which-experiences-are-influential-for-your-agent
공식 코드Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over Dropout
https://github.com/talhabozkus/tsp_23_supplementary_file
공식 코드Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy Optimization
https://github.com/tanchongmin/bttt
공식 코드Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test Environments
https://github.com/tanchongmin/learning-fast-and-slow
공식 코드Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments
https://github.com/tanmayshankar/RCNN_MDP
공식 코드Reinforcement Learning via Recurrent Convolutional Neural Networks
https://github.com/tanyuqian/learning-data-manipulation
공식 코드https://github.com/taodav/aux-inputs
공식 코드https://github.com/taoyds/test-suite-sql-eval
공식 코드https://github.com/tarekshohdy/ppo_sofa_soft_legged_robot
공식 코드Development of a PPO-Reinforcement Learned Walking Tripedal Soft-Legged Robot using SOFA
https://github.com/tarungog/torsionnet_paper_version
공식 코드TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
https://github.com/tatsu432/DR-estimator-OPE-large-action
공식 코드Doubly Robust Estimator for Off-Policy Evaluation with Large Action Spaces
https://github.com/tavlab-iiitd/VacSIM
공식 코드VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning
https://github.com/tbanker/MPCritic
공식 코드MPCritic: A plug-and-play MPC architecture for reinforcement learning
https://github.com/tbasaklar/PDMORL-Preference-Driven-Multi-Objective-Reinforcement-Learning-Algorithm
공식 코드PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm
https://github.com/tdelgado00/learning-synthesis
공식 코드Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach
https://github.com/team-daniel/advice
공식 코드Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
https://github.com/tedmoskovitz/DOPE
공식 코드Tactical Optimism and Pessimism for Deep Reinforcement Learning
https://github.com/tedmoskovitz/TOP
공식 코드Tactical Optimism and Pessimism for Deep Reinforcement Learning
https://github.com/tedmoskovitz/WNPG
공식 코드Efficient Wasserstein Natural Gradients for Reinforcement Learning
https://github.com/tegg89/magnet
공식 코드Deep Multi-Agent Reinforcement Learning with Relevance Graphs
https://github.com/tejas-deo/safe-navigation-training-autonomous-vehicles-using-deep-reinforcement-learning-in-carla
공식 코드Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLA
https://github.com/tencent-ailab/hok_env
공식 코드Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
https://github.com/tencent-ailab/hokoff
공식 코드Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks
https://github.com/tencent-ailab/rlogist
공식 코드RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning
https://github.com/tencent-ailab/tleague_projpage
공식 코드TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning
https://github.com/tencentarc/seed-bench-r1
공식 코드Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1
https://github.com/tengxiaoliu/rlet
공식 코드RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees
https://github.com/tensorflow/models
공식 코드https://github.com/tensorflow/tensor2tensor
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.