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AI Olympics challenge with Evolutionary Soft Actor Critic

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arxiv 2409.01104 v2 pith:JJSRPLLA submitted 2024-09-02 cs.RO cs.AIcs.LGcs.NE

classification cs.ROcs.AIcs.LGcs.NE
keywords approachdescribeevolutionaryolympicssolutionactoralgorithmsbeen
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In the following report, we describe the solution we propose for the AI Olympics competition held at IROS 2024. Our solution is based on a Model-free Deep Reinforcement Learning approach combined with an evolutionary strategy. We will briefly describe the algorithms that have been used and then provide details of the approach

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VIMPPI: Enhancing Model Predictive Path Integral Control with Variational Integration for Underactuated Systems

    eess.SY 2025-05 conditional novelty 5.0 of 10

    Using a variational integrator inside MPPI rollouts lets the controller plan 4-20 times further ahead, improving balance uptime on underactuated double pendulums.

  2. Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks

    cs.RO 2025-05 conditional novelty 3.0 of 10

    The authors' AR-EAPO controller achieves high simulated scores on swing-up tasks for acrobot and pendubot under increased disturbances by widening initial-state variance and shortening effective horizon during training.

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