  The objectives of option hedging/trading extend beyond mere protection
against downside risks, with a desire to seek gains also driving agent's
strategies. In this study, we showcase the potential of robust risk-aware
reinforcement learning (RL) in mitigating the risks associated with
path-dependent financial derivatives. We accomplish this by leveraging a policy
gradient approach that optimises robust risk-aware performance criteria. We
specifically apply this methodology to the hedging of barrier options, and
highlight how the optimal hedging strategy undergoes distortions as the agent
moves from being risk-averse to risk-seeking. As well as how the agent
robustifies their strategy. We further investigate the performance of the hedge
when the data generating process (DGP) varies from the training DGP, and
demonstrate that the robust strategies outperform the non-robust ones.
