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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/giseung-park/maxmin-morl

공식 코드

The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm

https://github.com/giuliapuntoit/RL-framework-iot

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RL-IoT: Reinforcement Learning to Interact with IoT Devices

https://github.com/giuse/DNE

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Playing Atari with Six Neurons

https://github.com/gjp1203/nui_in_madrl

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Negative Update Intervals in Deep Multi-Agent Reinforcement Learning

https://github.com/gkahn13/GtS

공식 코드

Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight

https://github.com/gkahn13/LaND

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LaND: Learning to Navigate from Disengagements

https://github.com/gkahn13/gcg

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Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

https://github.com/gkswamy98/fast_irl

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Inverse Reinforcement Learning without Reinforcement Learning

https://github.com/gkthom/lagrseq

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LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying

https://github.com/glample/Arnold

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Playing FPS Games with Deep Reinforcement Learning

https://github.com/gmc-drl/surr-rlde

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Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study

https://github.com/gml16/rl-medical

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Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images

https://github.com/gncs/molgym

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Reinforcement Learning for Molecular Design Guided by Quantum Mechanics

https://github.com/golsun/deep-RL-time-series

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Deep reinforcement learning for time series: playing idealized trading games

https://github.com/gomerudo/nas-env

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Learning to reinforcement learn for Neural Architecture Search

https://github.com/gomiss/action-balance-exploration

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Exploring Unknown States with Action Balance

https://github.com/goncamateus/Planning-the-path-with-rl

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Planning the path with Reinforcement Learning: Optimal Robot Motion Planning in RoboCup Small Size League Environments

https://github.com/google-deepmind/dmc_vision_benchmark

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DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors

https://github.com/google-research-datasets/Education-Dialogue-Dataset

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Multi-turn Reinforcement Learning from Preference Human Feedback

https://github.com/google-research-datasets/simulated-dialogue

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Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems

https://github.com/google-research/batch_rl

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An Optimistic Perspective on Offline Reinforcement Learning

https://github.com/google-research/big_vision

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Tuning computer vision models with task rewards

https://github.com/google-research/clevr_robot_env

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Language as an Abstraction for Hierarchical Deep Reinforcement Learning

https://github.com/google-research/deep_ope

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Benchmarks for Deep Off-Policy Evaluation

https://github.com/google-research/football

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Google Research Football: A Novel Reinforcement Learning Environment

https://github.com/google-research/google-research

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Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

https://github.com/google-research/google-research/tree/master/compositional_rl

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Adversarial Environment Generation for Learning to Navigate the Web

https://github.com/google-research/google-research/tree/master/distracting_control

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The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels

https://github.com/google-research/google-research/tree/master/dpok

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DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

https://github.com/google-research/google-research/tree/master/experience_replay

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Revisiting Fundamentals of Experience Replay

https://github.com/google-research/google-research/tree/master/generalization_representations_rl_aistats22

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On the Generalization of Representations in Reinforcement Learning

https://github.com/google-research/google-research/tree/master/hyperbolic_discount

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Hyperbolic Discounting and Learning over Multiple Horizons

https://github.com/google-research/google-research/tree/master/memento

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On Catastrophic Interference in Atari 2600 Games

https://github.com/google-research/google-research/tree/master/mico

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MICo: Improved representations via sampling-based state similarity for Markov decision processes

https://github.com/google-research/google-research/tree/master/mol_dqn

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Optimization of Molecules via Deep Reinforcement Learning

https://github.com/google-research/google-research/tree/master/munchausen_rl

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Munchausen Reinforcement Learning

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