  We propose a novel model-based reinforcement learning algorithm -- Dynamics
Learning and predictive control with Parameterized Actions (DLPA) -- for
Parameterized Action Markov Decision Processes (PAMDPs). The agent learns a
parameterized-action-conditioned dynamics model and plans with a modified Model
Predictive Path Integral control. We theoretically quantify the difference
between the generated trajectory and the optimal trajectory during planning in
terms of the value they achieved through the lens of Lipschitz Continuity. Our
empirical results on several standard benchmarks show that our algorithm
achieves superior sample efficiency and asymptotic performance than
state-of-the-art PAMDP methods.
