https://github.com/facebookresearch/mbrl-lib
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS
Modular Multitask Reinforcement Learning with Policy Sketches
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations
Rainbow: Combining Improvements in Deep Reinforcement Learning
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Practical Deep Reinforcement Learning Approach for Stock Trading
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.