  We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement
Learning (RL) problems, where the goal is to find a policy using data from
several tasks represented by Markov Decision Processes (MDPs) that can be
updated by one step of stochastic policy gradient for the realized MDP. In
particular, using stochastic gradients in MAML update steps is crucial for RL
problems since computation of exact gradients requires access to a large number
of possible trajectories. For this formulation, we propose a variant of the
MAML method, named Stochastic Gradient Meta-Reinforcement Learning (SG-MRL),
and study its convergence properties. We derive the iteration and sample
complexity of SG-MRL to find an $\epsilon$-first-order stationary point, which,
to the best of our knowledge, provides the first convergence guarantee for
model-agnostic meta-reinforcement learning algorithms. We further show how our
results extend to the case where more than one step of stochastic policy
gradient method is used at test time. Finally, we empirically compare SG-MRL
and MAML in several deep RL environments.
