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Behavioural cloning
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KamyarGh/rl_swiss
공식 코드A Divergence Minimization Perspective on Imitation Learning Methods
https://github.com/Miffyli/minecraft-bc
공식 코드https://github.com/NathanGavenski/ABCO
공식 코드https://github.com/NathanGavenski/CILO
공식 코드Explorative Imitation Learning: A Path Signature Approach for Continuous Environments
https://github.com/NathanGavenski/IUPE
공식 코드https://github.com/SilviaSapora/evil
공식 코드EvIL: Evolution Strategies for Generalisable Imitation Learning
https://github.com/TeaPearce/Counter-Strike_Behavioural_Cloning
공식 코드Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
https://github.com/alexbeesonwarwick/offlinerlconstrainedensemble
공식 코드Balancing policy constraint and ensemble size in uncertainty-based offline reinforcement learning
https://github.com/chenwang4/oilad
공식 코드OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences
https://github.com/ercumentilhan/naive-advice-imitation
공식 코드Action Advising with Advice Imitation in Deep Reinforcement Learning
https://github.com/joonaspu/video-game-behavioural-cloning
공식 코드https://github.com/nathangavenski/sail
공식 코드https://github.com/resuldagdanov/robot-learning-human-trust
공식 코드https://github.com/vzhong/language_feedback_models
공식 코드https://github.com/ercumentilhan/advice-imitation-reuse
Action Advising with Advice Imitation in Deep Reinforcement Learning
https://github.com/jackal-u/CSGOAPI
Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
https://github.com/nathangavenski/il-datasets
https://github.com/twni2016/f-IRL
A Divergence Minimization Perspective on Imitation Learning Methods
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.