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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/emi-group/evorl

공식 코드

EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning

https://github.com/emi-group/gpbt-pl

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Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement Learning

https://github.com/emily-halina/brisket

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Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI

https://github.com/emptyjackson/unifloral

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A Clean Slate for Offline Reinforcement Learning

https://github.com/enajx/HebbianMetaLearning

공식 코드

Meta-Learning through Hebbian Plasticity in Random Networks

https://github.com/engineerkong/emorl

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EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-Tuning

https://github.com/enjeeneer/PEARL

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Low Emission Building Control with Zero-Shot Reinforcement Learning

https://github.com/enjeeneer/conservative-world-models

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Zero-Shot Reinforcement Learning from Low Quality Data

https://github.com/enjeeneer/zero-shot-rl

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Zero-Shot Reinforcement Learning from Low Quality Data

https://github.com/enuzhin/ur

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Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problems

https://github.com/epignatelli/navix

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NAVIX: Scaling MiniGrid Environments with JAX

https://github.com/episodeyang/e-maml

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Some Considerations on Learning to Explore via Meta-Reinforcement Learning

https://github.com/erablab/value_iteration_for_learning_concurrently_executable_robotic_control_tasks

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Value Iteration for Learning Concurrently Executable Robotic Control Tasks

https://github.com/ercumentilhan/advice-imitation-reuse

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Learning on a Budget via Teacher Imitation

https://github.com/ercumentilhan/advice-novelty

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Student-Initiated Action Advising via Advice Novelty

https://github.com/ercumentilhan/naive-advice-imitation

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Action Advising with Advice Imitation in Deep Reinforcement Learning

https://github.com/erg0dic/transmon_public

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Sample-efficient Model-based Reinforcement Learning for Quantum Control

https://github.com/ergoncugler/q-learning

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From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no Libraries

https://github.com/eric-mitchell/macaw

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Offline Meta-Reinforcement Learning with Advantage Weighting

https://github.com/ericavandersar/rl4pnc-survey

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Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges

https://github.com/erik-derner/symbolic-regression

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Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression

https://github.com/ermongroup/MA-AIRL

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Multi-Agent Adversarial Inverse Reinforcement Learning

https://github.com/ermongroup/MetaIRL

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Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

https://github.com/ermongroup/PatchDrop

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Learning When and Where to Zoom with Deep Reinforcement Learning

https://github.com/ernovoseller/DuelingPosteriorSampling

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Dueling Posterior Sampling for Preference-Based Reinforcement Learning

https://github.com/eserie/wax-ml

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WAX-ML: A Python library for machine learning and feedback loops on streaming data

https://github.com/eslam211/conservative-and-distributional-marl

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Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning

https://github.com/esraaelelimy/gtd_algos

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Deep Reinforcement Learning with Gradient Eligibility Traces

https://github.com/etalvitie/hdaggermc

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Self-Correcting Models for Model-Based Reinforcement Learning

https://github.com/eth-disco/rlp

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PUZZLES: A Benchmark for Neural Algorithmic Reasoning

https://github.com/eth-lre/pedagogicalrl

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From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning

https://github.com/ethancjackson/NoveltySearchLevenshtein

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Novelty Search for Deep Reinforcement Learning Policy Network Weights by Action Sequence Edit Metric Distance

https://github.com/ethanluoyc/optimal_transport_reward

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Optimal Transport for Offline Imitation Learning

https://github.com/ethz-asl/Learn-to-Calibrate

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Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning

https://github.com/ethz-asl/navrep

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NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments

https://github.com/eugenevinitsky/robust_RL_multi_adversary

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Robust Reinforcement Learning using Adversarial Populations

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