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/cathyhxh/ctds
공식 코드CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
https://github.com/cathywu/flow
공식 코드Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/causal-rl-anonymous/causal-rl
공식 코드Causal Reinforcement Learning using Observational and Interventional Data
https://github.com/causalml/adversarial-ope
공식 코드Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
https://github.com/causalml/bcrl
공식 코드Learning Bellman Complete Representations for Offline Policy Evaluation
https://github.com/causalml/doubly-robust-dropel
공식 코드Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
https://github.com/causalml/proximalrl
공식 코드https://github.com/cav-research-lab/sacla
공식 코드Neural Lyapunov Function Approximation with Self-Supervised Reinforcement Learning
https://github.com/cbellinger27/learning-when-to-observe-in-rl
공식 코드Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning
https://github.com/cbfinn/maml
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/cbfinn/maml_rl
공식 코드Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/ccolas/rl_stats
공식 코드A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms
https://github.com/cda-tum/mqtpredictor
공식 코드Compiler Optimization for Quantum Computing Using Reinforcement Learning
https://github.com/cdevin/objectattention
공식 코드Deep Object-Centric Representations for Generalizable Robot Learning
https://github.com/cds-mipt/rbexplore
공식 코드https://github.com/ceisenach/MPG
공식 코드https://github.com/cerc-aai/realtime_rl
공식 코드Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference
https://github.com/cerenaut/wmar
공식 코드Augmenting Replay in World Models for Continual Reinforcement Learning
https://github.com/cesaremagnetti/automaticusnavigation
공식 코드Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer
https://github.com/cevahir-koprulu/rm-guided-sprl
공식 코드https://github.com/cfpark00/rl-wall
공식 코드Decomposing Elements of Problem Solving: What "Math" Does RL Teach?
https://github.com/cgrivera/ai-arena
공식 코드The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learning
https://github.com/chainer/chainerrl
공식 코드https://github.com/chaitjo/learning-paradigms-for-tsp
공식 코드https://github.com/chancefocus/trials
공식 코드Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning
https://github.com/chandar-lab/CMAL_Hanabi
공식 코드Continuous Coordination As a Realistic Scenario for Lifelong Learning
https://github.com/chandar-lab/Lifelong-Hanabi
공식 코드Continuous Coordination As a Realistic Scenario for Lifelong Learning
https://github.com/chandar-lab/LoCA
공식 코드The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
https://github.com/chandar-lab/LoCA2
공식 코드Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
https://github.com/chandar-lab/coe
공식 코드Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
https://github.com/changmin-yu/ccwm_code
공식 코드Learning State Representations via Retracing in Reinforcement Learning
https://github.com/changon/resampling_heartsteps
공식 코드https://github.com/changyuchen347/semi-offline-rl
공식 코드Semi-Offline Reinforcement Learning for Optimized Text Generation
https://github.com/chaovven/SMIX
공식 코드SMIX($λ$): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning
https://github.com/charleshsc/CompoFormer
공식 코드Continual Task Learning through Adaptive Policy Self-Composition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.