Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks
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Abstract (arXiv)
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 Policy Optimization (AR-EAPO), a model-free reinforcement learning (RL) algorithm that combines average-reward RL and maximum entropy RL. Results demonstrate that our controller achieves improved performance and robustness scores compared to established baseline methods in both the acrobot and pendubot scenarios, without the need for a heavily engineered reward function or system model. The current results are applicable exclusively to the simulation stage setup.
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References · 10
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
- 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
- 2018Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewcited by 466
- 2021Maximum Entropy RL (Provably) Solves Some Robust RL Problemscited by 169
- 2020Average-reward model-free reinforcement learning: a systematic review and literature mappingcited by 19
- 2018Entropy Maximization for Markov Decision Processes Under Temporal Logic Constraintscited by 11
- 2023Solving the swing-up and balance task for the Acrobot and Pendubot with SACcited by 2
- 2024Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimationcited by 2
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