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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/anonicml2019/icml_2019_state_abstraction

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Learning State Abstractions for Transfer in Continuous Control

https://github.com/anonuser-532438/policyrankinganon

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Ranking Policy Decisions

https://github.com/anonymconference/knnr

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$K$-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic Control

https://github.com/anonymize-author/agentrl

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Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving

https://github.com/anonymous-iclr22/trust-region-in-multi-agent-reinforcement-learning

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Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

https://github.com/anonymous902109/iters

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Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification

https://github.com/anonymous902109/raccer

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RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning

https://github.com/anonymous902109/reccover

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ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning

https://github.com/anonymous902109/sgrl

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Semifactual Explanations for Reinforcement Learning

https://github.com/anonymousidforsubmission/guidedlight

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GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents

https://github.com/anonymousresearcher5642/functionencoderrl

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Zero-Shot Reinforcement Learning via Function Encoders

https://github.com/anqili/mahalo

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MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations

https://github.com/anshuln/Diora_with_rules

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Rule Augmented Unsupervised Constituency Parsing

https://github.com/anthropics/hh-rlhf

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Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

https://github.com/anyleopeace/DURLECA

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Reinforced Epidemic Control: Saving Both Lives and Economy

https://github.com/aopolin-lv/DecisionMamba

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Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL

https://github.com/apeterswu/RL4NMT

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A Study of Reinforcement Learning for Neural Machine Translation

https://github.com/apexrl/AORPO

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Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts

https://github.com/apexrl/EBIL-torch

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Energy-Based Imitation Learning

https://github.com/apexrl/coil

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Curriculum Offline Imitation Learning

https://github.com/apexrl/diff4rlsurvey

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Diffusion Models for Reinforcement Learning: A Survey

https://github.com/apexrl/gcrl-collection

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Goal-Conditioned Reinforcement Learning: Problems and Solutions

https://github.com/apple/ml-diffucoder

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DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation

https://github.com/apple/ml-uwac

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Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

https://github.com/applied-machine-learning-lab/dt4ier

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Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention

https://github.com/applied-machine-learning-lab/gave

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Generative Auto-Bidding with Value-Guided Explorations

https://github.com/applied-machine-learning-lab/kuaisim

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KuaiSim: A Comprehensive Simulator for Recommender Systems

https://github.com/applied-machine-learning-lab/rmtl

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Multi-Task Recommendations with Reinforcement Learning

https://github.com/aptx395/deep-spiking-q-networks

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Human-Level Control through Directly-Trained Deep Spiking Q-Networks

https://github.com/aqasch/solving_syk_model_with_rl

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Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware

https://github.com/aqeel13932/APES

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APES: a Python toolbox for simulating reinforcement learning environments

https://github.com/aqeelanwar/PEDRA

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Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer Learning

https://github.com/araffin/robotics-rl-srl

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S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning

https://github.com/aralab-unr/ReinforcementLearningWithGA

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Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization

https://github.com/aralab-unr/multi-actor-critic-ddpg-with-aubo

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AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay

https://github.com/arashjkh/field-demonstrations-of-mpc-and-rl-for-residential-and-commecial-hvac

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Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.