During the exploration phase, an agent collects samples without using a pre-specified reward function. After the exploration phase, a reward function is given, and the agent uses samples collected during the exploration phase to computeanear-optimalpolicy.

We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs).

In this work, we provide a non-asymptotic analysis for two timescale actor-critic methods under non-i.i.d.