Risk-Averse Offline Reinforcement Learning

Understand

Training Reinforcement Learning (RL) agents in high-stakes applications might be too prohibitive due to the risk associated to exploration.

• Thus, the agent can only use data previously collected by safe policies.
• While previous work considers optimizing the average performance using offline data, we focus on optimizing a risk-averse criteria, namely the CVaR.
• In particular, we present the Offline Risk-Averse Actor-Critic (O-RAAC), a model-free RL algorithm that is able to learn risk-averse policies in a fully offline setting.