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/karthikncode/Grounded-RL-Transfer
공식 코드Grounding Language for Transfer in Deep Reinforcement Learning
https://github.com/kasunweerkoon/VAPOR
공식 코드VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning
https://github.com/katerakelly/oyster
공식 코드Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
https://github.com/kavosh8/Lip
공식 코드https://github.com/kay-ck/bsc-attack
공식 코드Attacking Video Recognition Models with Bullet-Screen Comments
https://github.com/kazizzad/BDQN-MxNet-Gluon
공식 코드https://github.com/kchua/handful-of-trials
공식 코드Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
https://github.com/kclary/variability-RL
공식 코드Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments
https://github.com/kdally/fault-tolerant-flight-control-drl
공식 코드Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control
https://github.com/kdkyum/RatchetDRL
공식 코드Deep reinforcement learning for feedback control in a collective flashing ratchet
https://github.com/kdl-umass/xai-interventional-robustness
공식 코드Measuring Interventional Robustness in Reinforcement Learning
https://github.com/kebaek/minigrid
공식 코드Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning
https://github.com/kelaxon/ssr-zero
공식 코드SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation
https://github.com/kelvinguu/lang2program
공식 코드From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
https://github.com/kenchan0226/keyphrase-generation-rl
공식 코드Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
https://github.com/kengz/SLM-Lab
공식 코드https://github.com/kenjyoung/MinAtar
공식 코드MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments
https://github.com/kenjyoung/model_generalization_code_supplement
공식 코드The Benefits of Model-Based Generalization in Reinforcement Learning
https://github.com/kepei1106/ARAML
공식 코드ARAML: A Stable Adversarial Training Framework for Text Generation
https://github.com/keqinw/man
공식 코드https://github.com/keraJLi/synthetic-gymnax
공식 코드https://github.com/kesenzhao/dt4rec
공식 코드User Retention-oriented Recommendation with Decision Transformer
https://github.com/kevinghst/lightweight_rl_probe
공식 코드Light-weight probing of unsupervised representations for Reinforcement Learning
https://github.com/kevinhuang8/cem-gd
공식 코드CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
https://github.com/kevinkoo001/ropf
공식 코드https://github.com/kevslinger/dtqn
공식 코드Deep Transformer Q-Networks for Partially Observable Reinforcement Learning
https://github.com/keynans/HypeRL
공식 코드Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
https://github.com/keyshor/dirl
공식 코드Compositional Reinforcement Learning from Logical Specifications
https://github.com/keyshor/spectrl_tool
공식 코드A Composable Specification Language for Reinforcement Learning Tasks
https://github.com/khanhptnk/bandit-nmt
공식 코드Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
https://github.com/khanhptnk/iliad
공식 코드https://github.com/kibeomkim/gace-gdan
공식 코드Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning
https://github.com/kinalmehta/epymarl_spectral
공식 코드Effects of Spectral Normalization in Multi-agent Reinforcement Learning
https://github.com/kinalmehta/marl-jax
공식 코드https://github.com/kindredresearch/SenseAct
공식 코드Benchmarking Reinforcement Learning Algorithms on Real-World Robots
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