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Efficient Exploration
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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/ofirnabati/neural-linear-bandits-with-likelihood-matching
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
https://github.com/oh-lab/UTE-Uncertainty-aware-Temporal-Extension-
Randomized Value Functions via Multiplicative Normalizing Flows
https://github.com/opendilab/DI-engine
https://github.com/ortec/euro-neurips-vrp-2022-quickstart
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood
https://github.com/pkassraie/NNUCB
https://github.com/qdevpsi3/randomized-value-iteration
Generalization and Exploration via Randomized Value Functions
https://github.com/rahlk/ConEX
ConEx: Efficient Exploration of Big-Data System Configurations for Better Performance
https://github.com/rahlk/ConEX__Replication_Package
ConEx: Efficient Exploration of Big-Data System Configurations for Better Performance
https://github.com/ramanans1/plan2explore
https://github.com/ray-project/ray/tree/master/rllib
State Entropy Maximization with Random Encoders for Efficient Exploration
https://github.com/rle-foundation/rlexplore
https://github.com/ronuchit/GLIB-AAAI-2021
https://github.com/s-gupta/map-plan-baseline
https://github.com/sauxpa/neural_exploration
https://github.com/seungjaeryanlee/rldb
https://github.com/shadowyzy/NIPS2019-MineRL-Competition-solution
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
https://github.com/shamelmerchant/keras-molecules
Automatic chemical design using a data-driven continuous representation of molecules
https://github.com/skumar9876/FCRL
Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning
https://github.com/soran-ghaderi/torchebm
https://github.com/tensorflow/models
https://github.com/tensorflow/models/tree/master/research/deep_contextual_bandits
https://github.com/tevang/keras-molecules
Automatic chemical design using a data-driven continuous representation of molecules
https://github.com/thomashirtz/noisy-networks
https://github.com/tianjunz/NovelD
https://github.com/trotsky1997/mathblackbox
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning
https://github.com/tum-daml/magnet
Automatic chemical design using a data-driven continuous representation of molecules
https://github.com/uclaml/neuralucb
https://github.com/uncharted-technologies/risk-and-uncertainty
Estimating Risk and Uncertainty in Deep Reinforcement Learning
https://github.com/uoe-agents/robotic-warehouse
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
https://github.com/vectorinstitute/neuralkernelbandits
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
https://github.com/victorchan314/cs287_final_project
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
https://github.com/waterhorse1/Pearl_relabel
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
https://github.com/xiaohan2012/efficient-rashomon-rule-set
Efficient Exploration of the Rashomon Set of Rule Set Models
https://github.com/xuanhungho/nsga
NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm
https://github.com/yaringal/HeteroscedasticDropoutUncertainty
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.