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/sophiagu/RLF
공식 코드Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies
https://github.com/sophieyueguo/explainable_action_advising
공식 코드Explainable Action Advising for Multi-Agent Reinforcement Learning
https://github.com/soroush-bn/ProtoX
공식 코드ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
https://github.com/sotetsuk/alpha-dimt-icmlws
공식 코드Neural Sequence Model Training via $α$-divergence Minimization
https://github.com/sotetsuk/pgx
공식 코드Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
https://github.com/sowmya-mp/rl_a3c_pytorch
공식 코드Visual Transfer between Atari Games using Competitive Reinforcement Learning
https://github.com/sparisi/td-reg
공식 코드https://github.com/sparisi/visit-value-explore
공식 코드Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning
https://github.com/spcl/x1
공식 코드https://github.com/spitis/deepnorms
공식 코드An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality
https://github.com/spitis/mocoda
공식 코드https://github.com/spitis/mrl
공식 코드Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
https://github.com/spl-bgu/polyplan
공식 코드https://github.com/spotify-research/RIPS_KDD2020
공식 코드Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
https://github.com/spotify-research/il-by-rl
공식 코드https://github.com/sreejank/Abstract_Neural_Metamers
공식 코드Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning
https://github.com/sreejank/Compositional_MetaRL
공식 코드Meta-Learning of Structured Task Distributions in Humans and Machines
https://github.com/sreejank/language_and_programs
공식 코드Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
https://github.com/sri-aic/22-llaama-ixdrl-data
공식 코드Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning
https://github.com/sri-aic/23-xai-ixdrl-data
공식 코드IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
https://github.com/sri-aic/ixdrl
공식 코드IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
https://github.com/sriram94/confidenceawareicrl
공식 코드https://github.com/sriram94/matlql
공식 코드Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
https://github.com/sriram94/multiagentadvisorqlearning
공식 코드https://github.com/srivathsanb14/mofgpt
공식 코드MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models
https://github.com/srsohn/msgi
공식 코드Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
https://github.com/srsohn/shortest-path-rl
공식 코드Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
https://github.com/srsohn/subtask-graph-execution
공식 코드Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
https://github.com/srzer/LaMo-2023
공식 코드Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning
https://github.com/ssg-research/ad3-action-distribution-divergence-detector
공식 코드https://github.com/ssm-lab/rl4mt
공식 코드Complex Model Transformations by Reinforcement Learning with Uncertain Human Guidance
https://github.com/ssnl/poisson_quasimetric_embedding
공식 코드https://github.com/ssokota/capi
공식 코드Solving Common-Payoff Games with Approximate Policy Iteration
https://github.com/ssokota/tiny-hanabi
공식 코드Solving Common-Payoff Games with Approximate Policy Iteration
https://github.com/st-tech/zr-obp
공식 코드Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.