https://github.com/sukiboo/hyperpersonalization
공식 코드On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks
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/sukiboo/policy_entropy
공식 코드Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks
https://github.com/sultanalnahian/Reverse-Frogger-A3PS
공식 코드Influencing Reinforcement Learning through Natural Language Guidance
https://github.com/sumanabasu/On-the-Challenges-of-using-Reinforcement-Learning-in-Precision-Drug-Dosing-Delay-and-Prolongedness-
공식 코드https://github.com/sunblaze-ucb/rl-generalization
공식 코드https://github.com/sunchipsster1/conspec
공식 코드Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL
https://github.com/suneelbelkhale/model-based-meta-rl-for-flight
공식 코드Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
https://github.com/suninghuang19/cfp
공식 코드https://github.com/suninghuang19/dittogym
공식 코드https://github.com/suquark/hoplite
공식 코드Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems
https://github.com/sureli/laber
공식 코드https://github.com/sureli/sgqn
공식 코드Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning
https://github.com/susanbao/rmarl_code
공식 코드What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
https://github.com/sustechgameai/dorl
공식 코드Reinforcement Learning with Dual-Observation for General Video Game Playing
https://github.com/sustechgameai/mfedrl
공식 코드State Space Closure: Revisiting Endless Online Level Generation via Reinforcement Learning
https://github.com/suyoung-lee/Episodic-Backward-Update
공식 코드Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update
https://github.com/suyoung-lee/ldm
공식 코드Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture
https://github.com/svmgrg/fma-pg
공식 코드A general class of surrogate functions for stable and efficient reinforcement learning
https://github.com/svott03/RamseyTheoryRL
공식 코드RamseyRL: A Framework for Intelligent Ramsey Number Counterexample Searching
https://github.com/swan-utokyo/deir
공식 코드DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards
https://github.com/swarmrl/swarmrl
공식 코드Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning
https://github.com/swarna-kpaul/neoplanner
공식 코드Sequential Planning in Large Partially Observable Environments guided by LLMs
https://github.com/swaroop-nath/rl-qfs
공식 코드Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning
https://github.com/sweetice/POPO
공식 코드https://github.com/sweety-dm/interference-aware-deep-q-learning
공식 코드https://github.com/swordholdersh/mang2vec
공식 코드MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement Learning
https://github.com/swt-user/dmpo
공식 코드Direct Multi-Turn Preference Optimization for Language Agents
https://github.com/sycamore-1/ucb-moppo
공식 코드UCB-driven Utility Function Search for Multi-objective Reinforcement Learning
https://github.com/sygogo/alignment_beamlines_rl
공식 코드Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines
https://github.com/sympcheck/neuralsymptomchecker
공식 코드NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization
https://github.com/synthetic-automated-systems/ruder_mbot_rl
공식 코드Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning
https://github.com/synthlabsai/big-math
공식 코드Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
https://github.com/syyunn/finrl-dt
공식 코드Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading
https://github.com/szrlee/hyperagent
공식 코드Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
https://github.com/t3as/mad-arl
공식 코드https://github.com/t6-thu/H2O
공식 코드When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.