https://github.com/verlab/StraightToThePoint_CVPR_2020
공식 코드Straight to the Point: Fast-forwarding Videos via Reinforcement Learning Using Textual Data
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/verlab/TextDrivenVideoAcceleration_TPAMI_2022
공식 코드Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
https://github.com/vermouth1992/bracp
공식 코드BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement Learning
https://github.com/veronicachelu/functional-acceleration-for-pmd
공식 코드https://github.com/veviurko/to-the-max
공식 코드https://github.com/vicgalle/ARAMARL
공식 코드https://github.com/vicgalle/data-sharing
공식 코드https://github.com/victorzxy/meowjong
공식 코드Building a 3-Player Mahjong AI using Deep Reinforcement Learning
https://github.com/vik0/LangevinDQN
공식 코드https://github.com/vint-1/chipdiffusion
공식 코드https://github.com/vios-s/mri_sequential_active_sampling
공식 코드https://github.com/viptankz/btr
공식 코드Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PC
https://github.com/vision-cair/af-guide
공식 코드Guiding Online Reinforcement Learning with Action-Free Offline Pretraining
https://github.com/vita-epfl/CrowdNav
공식 코드https://github.com/vita-epfl/DyNav
공식 코드https://github.com/vita-group/diffses
공식 코드https://github.com/vita-group/symbolicpcc
공식 코드https://github.com/vitchyr/rlkit
공식 코드https://github.com/vittoriogiammarino/bootstrapping-rl-4-sequential-picking
공식 코드https://github.com/vittorione94/ostrichrl
공식 코드OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion
https://github.com/vivek3141/gg-bench
공식 코드https://github.com/vklabmipt/implicit-unlikelihood-training
공식 코드Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement Learning
https://github.com/vkurenkov/guided-es-by-differentiable-simulators
공식 코드Guiding Evolutionary Strategies by Differentiable Robot Simulators
https://github.com/vkurenkov/language-grounding-multigoal
공식 코드Task-Oriented Language Grounding for Language Input with Multiple Sub-Goals of Non-Linear Order
https://github.com/vlomonaco/crlmaze
공식 코드Continual Reinforcement Learning in 3D Non-stationary Environments
https://github.com/vmartinezalvarez/DyNODE
공식 코드DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control
https://github.com/vmicheli/delta-iris
공식 코드https://github.com/vmicheli/lm-butlers
공식 코드https://github.com/volcengine/verl
공식 코드ReTool: Reinforcement Learning for Strategic Tool Use in LLMs
https://github.com/voot-t/guide-actor-critic
공식 코드https://github.com/vsyrgkanis/policy_learning_continuous_actions
공식 코드Semi-Parametric Efficient Policy Learning with Continuous Actions
https://github.com/vtnsisdd/rfrl-gym
공식 코드RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio Applications
https://github.com/vub-ai-lab/bdpi
공식 코드Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
https://github.com/vuoristo/MMAML
공식 코드Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
https://github.com/vuoristo/meta-gradients
공식 코드An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
https://github.com/vwlin/superstar
공식 코드DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.