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Q-Learning

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The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

<span style="color:grey; opacity: 0.6">( Image credit: Playing Atari with Deep Reinforcement Learning )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/openai/evolution-strategies-starter

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Evolution Strategies as a Scalable Alternative to Reinforcement Learning

https://github.com/openai/multiagent-particle-envs

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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/opendilab/ace

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ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency

https://github.com/oscartaoyupan/trafficlightcontrol_qs

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Traffic Light Control with Reinforcement Learning

https://github.com/osrlpaper/os_rl_papercode

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Deep Reinforcement Learning for Optimal Stopping with Application in Financial Engineering

https://github.com/overestimationbias/factors-of-influence-of-the-overestimation-bias

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Factors of Influence of the Overestimation Bias of Q-Learning

https://github.com/oxwhirl/opiq

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Optimistic Exploration even with a Pessimistic Initialisation

https://github.com/oxwhirl/wqmix

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Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/pagand/eco-light

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Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions

https://github.com/paintception/Deep-Quality-Value-Family-

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Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms

https://github.com/perrin-isir/afu

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AFU: Actor-Free critic Updates in off-policy RL for continuous control

https://github.com/petrosgk/L-Game-DQN

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A Deep Q-Learning Agent for the L-Game with Variable Batch Training

https://github.com/philip-amortila/l1-coverability

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Scalable Online Exploration via Coverability

https://github.com/philippe-eecs/idql

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IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies

https://github.com/pku-liang/HASCO

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HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation

https://github.com/pokaxpoka/sunrise

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SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

https://github.com/pooyanjamshidi/ElasticBench

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Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

https://github.com/ppsantos/rl-data-distribution-public

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The Impact of Data Distribution on Q-learning with Function Approximation

https://github.com/prabhatnagarajan/repro_dqn

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Deterministic Implementations for Reproducibility in Deep Reinforcement Learning

https://github.com/prajjwal1/rl_paradigm

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When should we prefer Decision Transformers for Offline Reinforcement Learning?

https://github.com/prisca-lab/q-learning_concentration

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Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of Mind

https://github.com/purewater0901/scq

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Strategically Conservative Q-Learning

https://github.com/qlan3/Explorer

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Maxmin Q-learning: Controlling the Estimation Bias of Q-learning

https://github.com/qq456cvb/doudizhu-C

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Combinational Q-Learning for Dou Di Zhu

https://github.com/qroa/qroa

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Towards Universal and Black-Box Query-Response Only Attack on LLMs with QROA

https://github.com/quantumiracle/mars

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A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

https://github.com/quantumiracle/nash-dqn

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A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

https://github.com/rail-berkeley/rlkit

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Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/rezabonyadi/atari_rl_arxiv

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Self Punishment and Reward Backfill for Deep Q-Learning

https://github.com/rlai-lab/Regularized-GradientTD

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Gradient Temporal-Difference Learning with Regularized Corrections

https://github.com/robfiras/ls-iq

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LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning

https://github.com/rodrigocoelho7/vqc_qlearning

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VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability

https://github.com/rogerxcn/lunar_lander_project

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Solving The Lunar Lander Problem under Uncertainty using Reinforcement Learning

https://github.com/romainducrocq/dqn-itscwpd

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Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection

https://github.com/rtealwitter/openstreets

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I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives

https://github.com/ryanxhr/ivr

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Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization

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