Several works haveshown this vulnerability via adversarial attacks, butexisting approaches onimproving therobustness ofDRL under this setting have limited success and lack for theoretical principles. We show that naively applying existing techniques on improving robustness for classification tasks,likeadversarialtraining,areineffectiveformanyRLtasks.

Theactiveagent trains (as a regular Double-DQN) up to the time of forking, at which point the passive agent is created asa'fork' (i.e.,with identical networkweights) oftheactiveagent.

Inthiswork, we carefully study the performance of PPO in cooperative multi-agent settings.

Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.

However, in adversarial environments, these methods can be non-robust, since the return is dependent on the strategies of both the decision-maker and adversary. Training a probabilistic model conditioned on observed return to predict action can fail to generalize, as the trajectories that achieve a return in the dataset might have done so due to a suboptimal behavior adversary.

Detecting and correcting bias is challenging, as ratings are subjective and unverifiable. Unlike previous works relying on prior knowledge or historical data, we propose a one-shot noise calibration process without any prior information.

In complex systems, we often observe complex global behavior emerge from a collection of agents interacting with each other in their environment, with each individual agent acting only on locally available information, without knowing thefullpicture.