Acrobot
9 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
These leaderboards are used to track progress in Acrobot
Latest papers with no code
Solving 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.
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 networks (SNNs) in combination with hyperdimensional computing.
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations
Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments.
Learning Environment Models with Continuous Stochastic Dynamics
We aim to provide insights into the decisions faced by the agent by learning an automaton model of environmental behavior under the control of an agent.
KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images
We demonstrate learning of Lagrangian dynamics from images on the dm_control pendulum, cartpole and acrobot environments.
Online Attentive Kernel-Based Temporal Difference Learning
Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA.
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.
Experimental Study on Reinforcement Learning-based Control of an Acrobot
Specifically, we study the control of angular velocity of the Acrobot, as well as control of its total energy, which is the sum of the kinetic and the potential energy.
LagNetViP: A Lagrangian Neural Network for Video Prediction
In this paper, we introduce a video prediction model where the equations of motion are explicitly constructed from learned representations of the underlying physical quantities.
Robot Design With Neural Networks, MILP Solvers and Active Learning
Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem.
