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Reinforcement Learning (RL)

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보존된 평가 기록: 3

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/farquhar13/rl_transmission_control

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Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning

https://github.com/fdeng18/dreamer-pro

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DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

https://github.com/fdojurado/pynetsim

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LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement Learning

https://github.com/federico-pizarrobejarano/safe-control-gym

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Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents

https://github.com/federicoVisi/GIMLeT

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Interactive Machine Learning of Musical Gesture

https://github.com/fedezocco/tmn5compco2-scipy

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Circular Microalgae-Based Carbon Control for Net Zero

https://github.com/feferna/AgentForge

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AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent Design

https://github.com/feng-yufei/ns-nli

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Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference

https://github.com/fernandoamat/slateope

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Control Variates for Slate Off-Policy Evaluation

https://github.com/fernandodemeer/rl_optimal_execution

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A Modular Framework for Reinforcement Learning Optimal Execution

https://github.com/ff93/parameter-based-value-functions

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Parameter-Based Value Functions

https://github.com/fffffarmer/pdt

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Future-conditioned Unsupervised Pretraining for Decision Transformer

https://github.com/filangel/qtrader

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Reinforcement Learning for Portfolio Management

https://github.com/filangelos/social_rl

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PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

https://github.com/filippoairaldi/mpcrl-cbf

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Probabilistically safe and efficient model-based Reinforcement Learning

https://github.com/filippoairaldi/mpcrl-for-ramp-metering

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Reinforcement Learning with Model Predictive Control for Highway Ramp Metering

https://github.com/financialcomputingucl/drl_for_active_high_frequency_trading

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Deep Reinforcement Learning for Active High Frequency Trading

https://github.com/flairox/jaxmarl

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JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

https://github.com/flexi-framework/drl_les

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Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations

https://github.com/flexi-framework/relexi

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Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems

https://github.com/flint-xf-fan/Byzantine-Federeated-RL

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Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

https://github.com/florensacc/snn4hrl

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Stochastic Neural Networks for Hierarchical Reinforcement Learning

https://github.com/florentdelgrange/vae_mdp

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Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report)

https://github.com/floringogianu/snrl

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Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective

https://github.com/flow-project/flow

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Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/flowersteam/EpidemiOptim

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EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

https://github.com/flowersteam/curious

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CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

https://github.com/flowersteam/eager

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EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL

https://github.com/flowersteam/geppg

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GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms

https://github.com/flowersteam/grounding_llms_with_online_rl

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Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning

https://github.com/flowersteam/lamorel

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Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning

https://github.com/flowersteam/rl-difference-testing

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How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments

https://github.com/flowersteam/rl_stats

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A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms

https://github.com/flowss/rlmutation

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Mutation Testing of Deep Reinforcement Learning Based on Real Faults

https://github.com/flxclxc/rl-graph-search

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Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies

https://github.com/fmaxgarcia/Meta-MDP

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A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning

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