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.
AI Olympics challenge with Evolutionary Soft Actor Critic
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abstract
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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Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks
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.