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Acrobot
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The acrobot system includes two joints and two links, where the joint between the two links is actuated. Initially, the links are hanging downwards, and the goal is to swing the end of the lower link up to a given height.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Bellman operator convergence enhancements in reinforcement learning algorithms
Learning global control of underactuated systems with Model-Based Reinforcement Learning
Real-Time Model Predictive Control for the Swing-Up Problem of an Underactuated Double Pendulum
RobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks
Linear Function Approximation as a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges
Solving the swing-up and balance task for the Acrobot and Pendubot with SAC
HyperSNN: A new efficient and robust deep learning model for resource constrained control applications
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations
Learning Environment Models with Continuous Stochastic Dynamics
Total energy-shaping control for mechanical systems via Control-by-Interconnection
KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images
Adaptive Online Value Function Approximation with Wavelets
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
Online Attentive Kernel-Based Temporal Difference Learning
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies
Experimental Study on Reinforcement Learning-based Control of an Acrobot
LagNetViP: A Lagrangian Neural Network for Video Prediction
Robot Design With Neural Networks, MILP Solvers and Active Learning
Meta-learning curiosity algorithms
Learning sparse representations in reinforcement learning
Adaptation to criticality through organizational invariance in embodied agents
Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces
Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents
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