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The transition from defined benefit to defined contribution pension plansshifts the responsibility for saving toward retirement from governments andinstitutions to the individuals. Determining optimal saving and investmentstrategy for individuals is paramount for stable financial stance and foravoiding poverty during work-life and retirement, and it is a particularlychallenging task in a world where form of employment and income trajectoryexperienced by different occupation groups are highly diversified. We introducea model in which agents learn optimal portfolio allocation and savingstrategies that are suitable for their heterogeneous profiles. We use deepreinforcement learning to train agents. The environment is calibrated withoccupation and age dependent income evolution dynamics. The research focuses onheterogeneous income trajectories dependent on agent profiles and incorporatesthe behavioural parameterisation of agents. The model provides a flexiblemethodology to estimate lifetime consumption and investment choices forheterogeneous profiles under varying scenarios.

Deep Reinforcement Learning for Optimal Investment and Saving Strategy  Selection in Heterogeneous Profiles: Intelligent Agents working towards  retirement