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    This study introduces Generative Adversarial Soft Actor-Critic (GASAC) for deep reinforcement learning. GASAC enables learning multimodal policies and improves stability in continuous control tasks.

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    Area of Science:

    • Deep Reinforcement Learning
    • Continuous Control
    • Policy Optimization

    Background:

    • Standard Soft Actor-Critic (SAC) uses factorized Gaussian policies, limiting expressiveness and ignoring action correlations.
    • Learning stochastic and multimodal policies is vital for complex deep reinforcement learning tasks.

    Purpose of the Study:

    • To enhance the policy representation in SAC beyond unimodal Gaussian distributions.
    • To develop a more stable and expressive policy learning method for continuous control.

    Main Methods:

    • Revisiting normalizing flows for SAC policies using the change of variable theorem.
    • Proposing a state-dependent nonvolume preserving (SD-NVP) architecture.
    • Introducing Generative Adversarial Soft Actor-Critic (GASAC) with a generative adversarial loss.

    Main Results:

    • GASAC effectively models multimodal policies, capturing complex action distributions.
    • The proposed methods demonstrate more stable learning in terms of cumulative return.
    • Experimental validation on multi-goal and MuJoCo continuous control tasks.

    Conclusions:

    • GASAC offers a powerful alternative to standard SAC for learning expressive and multimodal policies.
    • The approach improves learning stability and performance in challenging continuous control environments.
    • Normalizing flows and generative adversarial methods are effective for advancing deep reinforcement learning policies.