https://github.com/ruohliuq/c-morl
공식 코드C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
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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.
C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
RLBenchNet: The Right Network for the Right Reinforcement Learning Task
CrystalBox: Future-Based Explanations for Input-Driven Deep RL Systems
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees
Guided Exploration in Reinforcement Learning via Monte Carlo Critic Optimization
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
Understanding the Evolution of Linear Regions in Deep Reinforcement Learning
Addressing Function Approximation Error in Actor-Critic Methods
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
A Unified Framework for Alternating Offline Model Training and Policy Learning
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Active Hierarchical Exploration with Stable Subgoal Representation Learning
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.