https://github.com/brandontrabucco/efficient-hrl
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
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Continuous control in the context of playing games, especially within artificial intelligence (AI) and machine learning (ML), refers to the ability to make a series of smooth, ongoing adjustments or actions to control a game or a simulation. This is in contrast to discrete control, where the actions are limited to a set of specific, distinct choices. Continuous control is crucial in environments where precision, timing, and the magnitude of actions matter, such as driving a car in a racing game, controlling a character in a simulation, or managing the flight of an aircraft in a flight simulator.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Benchmarking Deep Reinforcement Learning for Continuous Control
Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
Catalyst.RL: A Distributed Framework for Reproducible RL Research
Benchmarking Deep Reinforcement Learning for Continuous Control
Benchmarking Deep Reinforcement Learning for Continuous Control
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Prediction and Generalisation over Directed Actions by Grid Cells
Adversarially Trained Actor Critic for Offline Reinforcement Learning
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
Addressing Function Approximation Error in Actor-Critic Methods
Simple random search provides a competitive approach to reinforcement learning
Addressing Function Approximation Error in Actor-Critic Methods
Learning Continuous Control Policies by Stochastic Value Gradients
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.