https://github.com/ycchen1989/Var-QuantumCircuits-DeepRL
공식 코드Variational Quantum Circuits for 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/yd-19/ham4drl
공식 코드https://github.com/yd-kwon/POMO
공식 코드POMO: Policy Optimization with Multiple Optima for Reinforcement Learning
https://github.com/ydchen0806/dbmim
공식 코드Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning
https://github.com/yeliu0930/Knowledge-guided-Open-Attribute-Value-Extraction-with-Reinforcement-Learning
공식 코드Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
https://github.com/yeliu918/QREFINE-PPO
공식 코드Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
https://github.com/yemam3/mod-rl-rcbf
공식 코드Safe Reinforcement Learning Using Robust Control Barrier Functions
https://github.com/yenchenlin/rl-attack-detection
공식 코드Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight
https://github.com/yeshenpy/awesome-evolutionary-reinforcement-learning
공식 코드https://github.com/yeshenpy/erl-re2
공식 코드https://github.com/yeshenpy/pmic
공식 코드PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
https://github.com/yetiminer/dogbarometer
공식 코드https://github.com/yf-he/instrumentaleval
공식 코드https://github.com/yfzhang114/r1_reward
공식 코드R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning
https://github.com/yg-smile/rl_vvc_dataset
공식 코드A Reinforcement Learning-based Volt-VAR Control Dataset and Testing Environment
https://github.com/yg211/acl20-ref-free-eval
공식 코드SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
https://github.com/yg211/summary-reward-no-reference
공식 코드Better Rewards Yield Better Summaries: Learning to Summarise Without References
https://github.com/yifan123/flow_grpo
공식 코드https://github.com/yifeizhou02/archer
공식 코드ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL
https://github.com/yihangyao/OASIS
공식 코드OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning
https://github.com/yihedeng9/duoguard
공식 코드DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM Guardrails
https://github.com/yihedeng9/openvlthinker
공식 코드https://github.com/yilun-wu/MetaL-Benchmark
공식 코드ALPaCA vs. GP-based Prior Learning: A Comparison between two Bayesian Meta-Learning Algorithms
https://github.com/yilundu/task_agnostic_dynamics_prior
공식 코드Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
https://github.com/yimengli46/bellman_point_goal
공식 코드Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation
https://github.com/yinchangchang/dac
공식 코드Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes
https://github.com/yingchengyang/BIRIS
공식 코드https://github.com/yingchengyang/CPPO
공식 코드Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
https://github.com/yingchengyang/Two-Stage-Attack
공식 코드Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning
https://github.com/yingfan-bot/mbpsrl
공식 코드Model-based Reinforcement Learning for Continuous Control with Posterior Sampling
https://github.com/yinglunxu/reward_poisoning_attack_drl
공식 코드Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning
https://github.com/yinxusen/learning_to_generalize
공식 코드https://github.com/yiqinyang/icq
공식 코드Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
https://github.com/ylkuo/ltl-zero-shot
공식 코드Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
https://github.com/yoavalon/MultiQuadcopterReinforcmentLearning
공식 코드https://github.com/yobibyte/atarigrandchallenge
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.