Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information

Understand

Recent research on reinforcement learning (RL) has suggested that trained agents are vulnerable to maliciously crafted adversarial samples.

• In this work, we show how such samples can be generalised from White-box and Grey-box attacks to a strong Black-box case, where the attacker has no knowledge of the agents, their training parameters and their training methods.
• We use sequence-to-sequence models to predict a single action or a sequence of future actions that a trained agent will make.
• First, we show our approximation model, based on time-series information from the agent, consistently predicts RL agents' future actions with high accuracy in a Black-box setup on a wide range of games and RL algorithms.