HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
arXiv preprint arXiv:2109.05490 , year=
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Comparative study of action factorization methods for hybrid action spaces across PPO/SAC/DQN finds branching dueling architectures effective and auto-regressive methods highest performing, with new VDN-PPO and PPO-MIX variants outperforming other PPO approaches.
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Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
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Revisiting Action Factorization for Complex Action Spaces
Comparative study of action factorization methods for hybrid action spaces across PPO/SAC/DQN finds branching dueling architectures effective and auto-regressive methods highest performing, with new VDN-PPO and PPO-MIX variants outperforming other PPO approaches.