Evolving curricula with regret-based environment design
Training generally-capable agents with reinforcement learning (RL) remains a significant
challenge. A promising avenue for improving the robustness of RL agents is through the use …
challenge. A promising avenue for improving the robustness of RL agents is through the use …
Emergent complexity and zero-shot transfer via unsupervised environment design
A wide range of reinforcement learning (RL) problems---including robustness, transfer
learning, unsupervised RL, and emergent complexity---require specifying a distribution of …
learning, unsupervised RL, and emergent complexity---require specifying a distribution of …
Robust reinforcement learning on state observations with learned optimal adversary
Explicable reward design for reinforcement learning agents
We study the design of explicable reward functions for a reinforcement learning agent while
guaranteeing that an optimal policy induced by the function belongs to a set of target …
guaranteeing that an optimal policy induced by the function belongs to a set of target …
Provably efficient black-box action poisoning attacks against reinforcement learning
Due to the broad range of applications of reinforcement learning (RL), understanding the
effects of adversarial attacks against RL model is essential for the safe applications of this …
effects of adversarial attacks against RL model is essential for the safe applications of this …
Security and Privacy Issues in Deep Reinforcement Learning: Threats and Countermeasures
Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI),
where agents interact with environments to learn policies for solving complex tasks. In recent …
where agents interact with environments to learn policies for solving complex tasks. In recent …