Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization

Understand

In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment.

• In real-world applications, test conditions may differ substantially from the training scenario and, therefore, focusing on pure reward maximization during training may lead to poor results at test time.
• In these cases, it is important to trade-off between performance and robustness while learning a policy.
• While several results exist for robust, model-based RL, the model-free case has not been widely investigated.