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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/yangjenny/ImbalancedLearningRL

공식 코드

Deep Reinforcement Learning for Multi-class Imbalanced Training

https://github.com/yangrui2015/riql

공식 코드

Towards Robust Offline Reinforcement Learning under Diverse Data Corruption

https://github.com/yinxusen/deepword

공식 코드

Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Games

https://github.com/yinxusen/dqn-zork

공식 코드

Comprehensible Context-driven Text Game Playing

https://github.com/yiqinyang/icq

공식 코드

Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning

https://github.com/youseob/pin

공식 코드

Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL

https://github.com/yubin-liu/hybrid-q-learning-network-approach-for-mplp

공식 코드

Route Planning for Last-Mile Deliveries Using Mobile Parcel Lockers: A Hybrid Q-Learning Network Approach

https://github.com/yudasong/hyq

공식 코드

Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient

https://github.com/yunfeiluo/autonomous-stock-trading

공식 코드

Agent Performing Autonomous Stock Trading under Good and Bad Situations

https://github.com/z-t-wang/phdthesis

공식 코드

Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement Learning

https://github.com/zbzhu99/madiff

공식 코드

MADiff: Offline Multi-agent Learning with Diffusion Models

https://github.com/zero8319/easpace

공식 코드

EASpace: Enhanced Action Space for Policy Transfer

https://github.com/zhao-song/Softmax-DQN

공식 코드

Revisiting the Softmax Bellman Operator: New Benefits and New Perspective

https://github.com/zhaoyizhou1123/mbrcsl

공식 코드

Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning

https://github.com/zhendong-wang/diffusion-policies-for-offline-rl

공식 코드

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

https://github.com/zhishenhuang/rlsamp

공식 코드

Reinforcement Learning for Sampling on Temporal Medical Imaging Sequences

https://github.com/zoulixin93/NICF

공식 코드

Neural Interactive Collaborative Filtering

https://github.com/zyh-pku/reinforcement-learning-and-market-making

공식 코드

Reinforcement Learning in High-frequency Market Making

https://github.com/zzbuzzard/pairvdn

공식 코드

PairVDN - Pair-wise Decomposed Value Functions

https://gitlab.com/sumo-rl/sumo_openai_gym

공식 코드

Urban Driving with Multi-Objective Deep Reinforcement Learning

https://gitlab.com/ymd_h/cpprb

공식 코드

ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

https://github.com/1jsingh/rl_navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/2023-MindSpore-1/ms-code-210/tree/main/DDAG

Continuous control with deep reinforcement learning

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/ras

Adversarial Learning of a Sampler Based on an Unnormalized Distribution

https://github.com/2023-MindSpore-1/ms-code-52

Playing Atari with Deep Reinforcement Learning

https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_11_optimization

Optimization of Molecules via Deep Reinforcement Learning

https://github.com/AIDefender/MyDiscor

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

https://github.com/Abdelhamid-bouzid/Multi-Agent-Deep-Deterministic-Policy-Gradient

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

https://github.com/Adrelf/DRL-navigation

Deep Reinforcement Learning with Double Q-learning

https://github.com/AgrawalAmey/safe-explorer

Continuous control with deep reinforcement learning

https://github.com/Ah31/maddpg_pytorch

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

https://github.com/AimoreRRD/AIMORE_GAME

Towards Symbolic Reinforcement Learning with Common Sense

https://github.com/AleXander-Tsui/MPE

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

https://github.com/AlexandraFilimokhina/MountainCarConrinious_DDPG

Continuous control with deep reinforcement learning

https://github.com/AmirNiaraki/Visual-Exploration

Visual Exploration and Energy-aware Path Planning via Reinforcement Learning

https://github.com/AmmarFayad/Behavioral-Actor-Critic

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

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