Jonaid Shianifar

Jonaid Shianifar, Michael Schukat, Karl Mason

Multi-objective reinforcement learning (MORL) enables agents to optimize vector-valued rewards while respecting user preferences. CAPQL, a preference-conditioned actor-critic method, achieves this by conditioning on weight vectors w and restricts data usage to the specific preferences under which it was collected, leaving off-policy data from other preferences unused. We introduce Hindsight Preference Replay (HPR), a simple and general replay augmentation strategy that retroactively relabels stored transitions with alternative preferences. This densifies supervision across the preference simplex without altering the CAPQL architecture or loss functions. Evaluated on six MO-Gymnasium locomotion tasks at a fixed 300000-step budget using expected utility (EUM), hypervolume (HV), and sparsity, HPR-CAPQL improves HV in five of six environments and EUM in four of six. On mo-humanoid-v5, for instance, EUM rises from $323\!\pm\!125$ to $1613\!\pm\!464$ and HV from 0.52M to 9.63M, with strong statistical support. mo-halfcheetah-v5 remains a challenging exception where CAPQL attains higher HV at comparable EUM. We report final summaries and Pareto-front visualizations across all tasks.

Jonaid Shianifar, Michael Schukat, Karl Mason

In this paper, we explore the optimization of hyperparameters for the Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms using the Tree-structured Parzen Estimator (TPE) in the context of robotic arm control with seven Degrees of Freedom (DOF). Our results demonstrate a significant enhancement in algorithm performance, TPE improves the success rate of SAC by 10.48 percentage points and PPO by 34.28 percentage points, where models trained for 50K episodes. Furthermore, TPE enables PPO to converge to a reward within 95% of the maximum reward 76% faster than without TPE, which translates to about 40K fewer episodes of training required for optimal performance. Also, this improvement for SAC is 80% faster than without TPE. This study underscores the impact of advanced hyperparameter optimization on the efficiency and success of deep reinforcement learning algorithms in complex robotic tasks.