A Bandit Framework for Optimal Selection of Reinforcement Learning Agents
This addresses the challenge of efficiently choosing reinforcement learning architectures in practical applications where interactions are expensive, though it is incremental as it builds on existing bandit and agent selection methods.
The paper tackles the problem of selecting the optimal reinforcement learning agent from a set with different inductive biases in costly environments without simulators, by proposing a multi-arm bandit framework that uses surrogate rewards to accelerate selection, and shows it consistently selects the optimal agent after finite steps while collecting more cumulative reward compared to sub-optimal or uniform selection.
Deep Reinforcement Learning has been shown to be very successful in complex games, e.g. Atari or Go. These games have clearly defined rules, and hence allow simulation. In many practical applications, however, interactions with the environment are costly and a good simulator of the environment is not available. Further, as environments differ by application, the optimal inductive bias (architecture, hyperparameters, etc.) of a reinforcement agent depends on the application. In this work, we propose a multi-arm bandit framework that selects from a set of different reinforcement learning agents to choose the one with the best inductive bias. To alleviate the problem of sparse rewards, the reinforcement learning agents are augmented with surrogate rewards. This helps the bandit framework to select the best agents early, since these rewards are smoother and less sparse than the environment reward. The bandit has the double objective of maximizing the reward while the agents are learning and selecting the best agent after a finite number of learning steps. Our experimental results on standard environments show that the proposed framework is able to consistently select the optimal agent after a finite number of steps, while collecting more cumulative reward compared to selecting a sub-optimal architecture or uniformly alternating between different agents.