Papers Acrobot
“Acrobot” 태그가 달린 논문 28편 · 필터 해제
BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies
The quest for interpretable reinforcement learning is a grand challenge for the deployment of autonomous decision-making systems in safety-critical applications. Modern deep reinforcement learning approaches, while power…
AcrobotDeep Reinforcement LearningDiversityreinforcement-learning+1Bellman operator convergence enhancements in reinforcement learning algorithms
This paper reviews the topological groundwork for the study of reinforcement learning (RL) by focusing on the structure of state, action, and policy spaces. We begin by recalling key mathematical concepts such as complet…
AcrobotDecision Makingreinforcement-learningReinforcement Learning+1Learning global control of underactuated systems with Model-Based Reinforcement Learning
This short paper describes our proposed solution for the third edition of the "AI Olympics with RealAIGym" competition, held at ICRA 2025. We employed Monte-Carlo Probabilistic Inference for Learning Control (MC-PILCO), …
AcrobotModel-based Reinforcement LearningReal-Time Model Predictive Control for the Swing-Up Problem of an Underactuated Double Pendulum
The 3rd AI Olympics with RealAIGym competition poses the challenge of developing a global policy that can swing up and stabilize an underactuated 2-link system Acrobot and/or Pendubot from any configuration in the state …
AcrobotModel Predictive ControlRobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model
Redundant manipulators, with their higher Degrees of Freedom (DOFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot coll…
AcrobotMotion PlanningAverage-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks
This report presents a solution for the swing-up and stabilisation tasks of the acrobot and the pendubot, developed for the AI Olympics competition at IROS 2024. Our approach employs the Average-Reward Entropy Advantage …
AcrobotReinforcement Learning (RL)Linear Function Approximation as a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges
Neural Network based approximations of the Value function make up the core of leading Policy Based methods such as Trust Regional Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). While this adds signifi…
AcrobotPolicy Gradient MethodsSolving the swing-up and balance task for the Acrobot and Pendubot with SAC
We present a solution of the swing-up and balance task for the pendubot and acrobot for the participation in the AI Olympics competition at IJCAI 2023. Our solution is based on the Soft Actor Crtic (SAC) reinforcement le…
AcrobotPositionreinforcement-learningReinforcement Learning (RL)HyperSNN: A new efficient and robust deep learning model for resource constrained control applications
In light of the increasing adoption of edge computing in areas such as intelligent furniture, robotics, and smart homes, this paper introduces HyperSNN, an innovative method for control tasks that uses spiking neural net…
AcrobotEdge-computingModel Predictive ControlA Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations
Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. However, implementing RL in hardware-efficient and bio-inspired ways remains a challenge. This paper presents a novel…
AcrobotDecision Makingreinforcement-learningReinforcement Learning+1Learning Environment Models with Continuous Stochastic Dynamics
Solving control tasks in complex environments automatically through learning offers great potential. While contemporary techniques from deep reinforcement learning (DRL) provide effective solutions, their decision-making…
AcrobotBenchmarkingDecision MakingDeep Reinforcement Learning+2Signal Novelty Detection as an Intrinsic Reward for Robotics
In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot’s environment. However, the feedback or reward is typical…
AcrobotAnomaly DetectionDisaster ResponseNovelty Detection+1Total energy-shaping control for mechanical systems via Control-by-Interconnection
Application of IDA-PBC to mechanical systems has received much attention in recent decades, but its application is still limited by the solvability of the so-called matching conditions. In this work, it is shown that tot…
AcrobotKeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images
We present KeyCLD, a framework to learn Lagrangian dynamics from images. Learned keypoints represent semantic landmarks in images and can directly represent state dynamics. We show that interpreting this state as Cartesi…
AcrobotvalidAdaptive Online Value Function Approximation with Wavelets
Using function approximation to represent a value function is necessary for continuous and high-dimensional state spaces. Linear function approximation has desirable theoretical guarantees and often requires less compute…
AcrobotTransfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
Transfer learning approaches in reinforcement learning aim to assist agents in learning their target domains by leveraging the knowledge learned from other agents that have been trained on similar source domains. For exa…
Acrobotreinforcement-learningReinforcement Learning (RL)Transfer Learning+1Online Attentive Kernel-Based Temporal Difference Learning
With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers fr…
AcrobotReinforcement Learning (RL)The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning
Iterated batch reinforcement learning (RL) is a growing subfield fueled by the demand from systems engineers for intelligent control solutions that they can apply within their technical and organizational constraints. Mo…
AcrobotModel Predictive ControlReinforcement Learning (RL)Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimod…
AcrobotmodelModel-based Reinforcement LearningLearning Synthetic Environments for Reinforcement Learning with Evolution Strategies
This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on…
Acrobotreinforcement-learningReinforcement LearningReinforcement Learning (RL)