Papers with Code 아카이브

Efficient Exploration

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

코드

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

https://github.com/behzadanksu/rlattack-dev

Noisy Networks for Exploration

https://github.com/bonniesjli/DQN_SR

Count-Based Exploration with the Successor Representation

https://github.com/brettin/keras-molecule

Automatic chemical design using a data-driven continuous representation of molecules

https://github.com/chainer/chainerrl

Noisy Networks for Exploration

https://github.com/chandar-lab/RLHive

Noisy Networks for Exploration

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

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

https://github.com/cshizhe/vln-duet

Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language Navigation

https://github.com/daniellawson9999/online-decision-transformer

Online Decision Transformer

https://github.com/facebookresearch/RandomizedValueFunctions

Randomized Value Functions via Multiplicative Normalizing Flows

https://github.com/facebookresearch/nle

BeBold: Exploration Beyond the Boundary of Explored Regions

https://github.com/gd-zhang/noisy-K-FAC

Noisy Natural Gradient as Variational Inference

https://github.com/harvard-edge/source-seeking

Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller

https://github.com/hsvgbkhgbv/TACTHMC

Stochastic Gradient Hamiltonian Monte Carlo

https://github.com/hsvgbkhgbv/Thermostat-assisted-continuously-tempered-Hamiltonian-Monte-Carlo-for-Bayesian-learning

Stochastic Gradient Hamiltonian Monte Carlo

https://github.com/hvarfner/CondGenVAE-BO

Automatic chemical design using a data-driven continuous representation of molecules

https://github.com/hw9603/DQfD-PyTorch

Noisy Networks for Exploration

https://github.com/hyunghona/emu

Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration

https://github.com/icaros-usc/dqd-rl

Scaling MAP-Elites to Deep Neuroevolution

https://github.com/ido90/roml-pearl

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

https://github.com/impredicative/irc-rss-feed-bot

Concurrent Meta Reinforcement Learning

https://github.com/johannah/bootstrap_dqn

Deep Exploration via Bootstrapped DQN

https://github.com/jondaa/CS236605FinalProject

Variance Networks: When Expectation Does Not Meet Your Expectations

https://github.com/junmokane/offline-PEARL

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

https://github.com/kevin-yu-0602/NeuralUCB_257

Neural Contextual Bandits with UCB-based Exploration

https://github.com/l4fl4m3/bmg

Bootstrapped Meta-Learning

https://github.com/leungjch/drug_VAE

Automatic chemical design using a data-driven continuous representation of molecules

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

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

https://github.com/lujiayou123/Off-Policy-Meta-Reinforcement-Learning-via-Unsupervised-Domain-Translation

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

https://github.com/med-air/surrol

Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot

https://github.com/mindspore-courses/Rainbow-MindSpore

Noisy Networks for Exploration

https://github.com/minfanzhang/noisy-K-FAC_added_flipout

Noisy Natural Gradient as Variational Inference

https://github.com/mj-will/pocomc-bilby

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

https://github.com/mlisicki/neuralkernelbandits

Online Limited Memory Neural-Linear Bandits with Likelihood Matching

https://github.com/mohammeddonia/RSCAM-GradNoise

Stochastic Gradient Hamiltonian Monte Carlo

https://github.com/mrahtz/learning-from-human-preferences

Deep Exploration via Bootstrapped DQN

https://github.com/nvidia/modulus

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

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