https://github.com/2groza/action-refined-temporal-difference
공식 코드Toward Causal-Aware RL: State-Wise Action-Refined Temporal Difference
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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.
Toward Causal-Aware RL: State-Wise Action-Refined Temporal Difference
Sample-efficient Real-time Planning with Curiosity Cross-Entropy Method and Contrastive Learning
Stochastic Actor-Executor-Critic for Image-to-Image Translation
Bayesian Policy Gradients via Alpha Divergence Dropout Inference
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
Continual Reinforcement Learning with Multi-Timescale Replay
DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs
An Unsupervised Video Game Playstyle Metric via State Discretization
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control Priors
TAAC: Temporally Abstract Actor-Critic for Continuous Control
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
Solving Compositional Reinforcement Learning Problems via Task Reduction
A Divergence Minimization Perspective on Imitation Learning Methods
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
World Model as a Graph: Learning Latent Landmarks for Planning
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Generalizable Episodic Memory for Deep Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.