Task-agnostic Exploration in Reinforcement Learning
This work addresses the problem of learning multiple skills or balancing conflicting objectives in RL for agents operating in complex environments, representing a novel approach rather than an incremental improvement.
The paper tackles the challenge of efficient exploration in reinforcement learning when no single reward function exists, proposing a task-agnostic RL framework where an agent explores without reward guidance and later finds near-optimal policies for multiple tasks using collected trajectories. The result is an algorithm, UCBZero, that achieves ε-optimal policies for N tasks with a sample complexity of Õ(log(N)H⁵SA/ε²) exploration episodes, along with a matching lower bound showing the log dependency on N is unavoidable.
Efficient exploration is one of the main challenges in reinforcement learning (RL). Most existing sample-efficient algorithms assume the existence of a single reward function during exploration. In many practical scenarios, however, there is not a single underlying reward function to guide the exploration, for instance, when an agent needs to learn many skills simultaneously, or multiple conflicting objectives need to be balanced. To address these challenges, we propose the \textit{task-agnostic RL} framework: In the exploration phase, the agent first collects trajectories by exploring the MDP without the guidance of a reward function. After exploration, it aims at finding near-optimal policies for $N$ tasks, given the collected trajectories augmented with \textit{sampled rewards} for each task. We present an efficient task-agnostic RL algorithm, \textsc{UCBZero}, that finds $ε$-optimal policies for $N$ arbitrary tasks after at most $\tilde O(\log(N)H^5SA/ε^2)$ exploration episodes. We also provide an $Ω(\log (N)H^2SA/ε^2)$ lower bound, showing that the $\log$ dependency on $N$ is unavoidable. Furthermore, we provide an $N$-independent sample complexity bound of \textsc{UCBZero} in the statistically easier setting when the ground truth reward functions are known.