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Acrobot

3260 papers • 126 benchmarks • 313 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.

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Most implemented papers

Criticality as it could be: Organizational invariance as self-organized criticality in embodied agents

Miguel Aguilera, M. G. Bedia•Mon Apr 17 2017

A conceptual model is constructed that, instead of specifying mechanistic requirements to generate criticality, exploits the maintenance of an organizational structure capable of reproducing critical behavior within a few universality classes of systems independently of their specific mechanisms or topologies.

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Adaptation to criticality through organizational invariance in embodied agents

Miguel Aguilera, M. G. Bedia•Tue Dec 12 2017

A simple learning rule is proposed that maintains an internal organizational structure from a specific family of systems at criticality that is implemented in artificial embodied agents controlled by a neural network maintaining a correlation structure randomly sampled from an Ising model at critical temperature.

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Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies

F. Hutter, Fábio Ferreira, Thomas Nierhoff•Sat Jan 23 2021

This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning as a bi-level optimization problem and represents an SE as a neural network by using Natural Evolution Strategies and a population of SE parameter vectors.

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Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?

B. Kégl, Gabriel Hurtado, Albert Thomas•Fri Jul 23 2021

It is found that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin, and that heteroscedasticity at training time, perhaps acting as a regularizer, improves predictions at longer horizons.

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Meta-learning curiosity algorithms

L. Kaelbling, Tomas Lozano-Perez, Ferran Alet, Martin Schneider•Tue Mar 10 2020

This work proposes a strategy for encoding curiosity algorithms as programs in a domain-specific language and searching, during a meta-learning phase, for algorithms that enable RL agents to perform well in new domains.

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Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations

H. Tran, Nathan Beck, Abhiramon Rajasekharan•Fri Feb 04 2022

A reward shaping method based on source embedding similarity that is applicable to domains with both discrete and continuous action spaces and does not outperform two baselines but does show an improvement in these discrete action spaces.

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Adaptive Online Value Function Approximation with Wavelets

Steven D. James, Michael Beukman, Michael Mitchley, Dean S. Wookey, G. Konidaris•Thu Apr 21 2022

It is proved that wavelets are both necessary and sufficient if the authors wish to construct a function approximator that can be adaptively refined without loss of precision, and that a fixed wavelet basis set performs comparably against the high-performing Fourier basis on Mountain Car and Acrobot.

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Total energy-shaping control for mechanical systems via Control-by-Interconnection

Joel Ferguson•Mon Jan 09 2023

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 total energy-shaping control of under-actuated mechanical systems has a control-by-interconnection interpretation. Using this interpretation, alternate matching conditions are formulated that defines constraints on the added energy, rather then the total closed-loop energy. It is additionally shown that, for systems that are under-actuated degree one with the mass matrix depending on a single coordinate, the kinetic energy matching conditions resolve to ODEs which can be evaluated numerically. Using this approach controllers are proposed for the benchmark cart-pole and acrobot systems.

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Signal Novelty Detection as an Intrinsic Reward for Robotics

Jiri Pospíchal, Martin Kubovčík, Iveta Dirgová Luptáková•Fri Mar 31 2023

An Autoencoder deep learning neural network was utilized as novelty detection for intrinsic rewards to guide the search process through a state space and achieved more efficient and accurate robot control in three of the four tasks when purely intrinsic rewards were used compared to standard extrinsic rewards.

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Acrobot | State-of-the-Art