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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/lerrel/gym-adv

Robust Adversarial Reinforcement Learning

https://github.com/lerrel/rllab-adv

Robust Adversarial Reinforcement Learning

https://github.com/letian-wang/asaprl

Accelerating Reinforcement Learning with Learned Skill Priors

https://github.com/lexfridman/deeptraffic

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/lflfdxfn/DPLAN-Implementation

Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data

https://github.com/lgcollins/tr-maml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/lgerrets/rl18-curiosity

Exploration by Random Network Distillation

https://github.com/lh-wang/ACC

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/liamcli/darts

DARTS: Differentiable Architecture Search

https://github.com/liampetti/DDPG

Continuous control with deep reinforcement learning

https://github.com/lich14/CDS

Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

https://github.com/lif31up/model-agnostic-meta-learning

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/lightning-uq-box/lightning-uq-box

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

https://github.com/ligy2016/gym

https://github.com/likaixin2000/screenspot-pro-gui-grounding

Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning

https://github.com/lil-lab/ciff

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

https://github.com/limbo018/dreamplace

On Joint Learning for Solving Placement and Routing in Chip Design

https://github.com/lina2360/HiSeqGan

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

https://github.com/ling-pan/res

The StarCraft Multi-Agent Challenge

https://github.com/lingweizhu/Pytorch-MunchausenActorCritic

Soft Actor-Critic for Discrete Action Settings

https://github.com/linhlpv/Offline-and-OfflineOnline-RL

AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

https://github.com/lippman1125/darts_notes

DARTS: Differentiable Architecture Search

https://github.com/liqichen6688/baycnn

Weight Uncertainty in Neural Networks

https://github.com/lireer/ricochet-robot-solver

Rainbow: Combining Improvements in Deep Reinforcement Learning

https://github.com/lit-leo/inner-convex-nn

Input Convex Neural Networks

https://github.com/liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow

Adversarial Learning for Neural Dialogue Generation

https://github.com/liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow

Deep Reinforcement Learning for Dialogue Generation

https://github.com/liuyuezhang/pyrl

Continuous control with deep reinforcement learning

https://github.com/liyang619/cole-platform

Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination

https://github.com/liyuan9988/deepfeatureproxyvariable

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation

https://github.com/llSourcell/OpenAI_Five_vs_Dota2_Explained

Emergence of Locomotion Behaviours in Rich Environments

https://github.com/llSourcell/SQL_Database_Optimization

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/lmzintgraf/varibad

Meta reinforcement learning as task inference

https://github.com/lollcat/RL-Process-Design

Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

https://github.com/lollcat/Soft-Actor-Critic

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

https://github.com/londoed/Gorila

Massively Parallel Methods for Deep Reinforcement Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.