A Multi-Objective Deep Reinforcement Learning Framework

Classical reinforcement learning maximizes a scalar return, whereas real tasks involve conflicting criteria such as time, energy, risk, and reward. Multi-objective RL represents them as a reward vector and seeks nondominated, Pareto-efficient policies. Traditional approaches often use fixed linear weights. Deep RL handles complex observations, but the field lacks a common architecture that makes multi-objective learning, preference handling, and policy organization interchangeable.

The central problem is to learn high-quality trade-offs without prematurely collapsing all objectives into one fixed score. Fixed weights can miss non-convex regions of the Pareto front; one policy may fail to cover diverse preferences, while many policies increase training and management costs. The desired system must also work beyond two objectives and remain compatible with DQN-style deep RL.

The work contributes three linked innovations. First, it builds a modular DQN-based MODRL framework. Second, it supports both single-policy and multi-policy organization. Third, it supports linear and nonlinear action selection. The fundamental distinction from many specialized methods is decoupling value estimation, preference representation, and policy management, allowing alternative deep-RL components to be composed within one testbed.

• �� Environment input: Deep Sea Treasure or Mountain Car supplies states and vector rewards.
• �� Value learning: DQN estimates action values while preserving multiple objective components.
• �� Action selection: linear methods combine objectives through weights; nonlinear methods use Pareto-oriented or other non-additive rules.
• �� Policy organization: single-policy learning encodes trade-offs in one policy; multi-policy learning maintains several preference or compromise policies.
• �� Evaluation: generated return vectors and policies are compared with the Pareto-optimal or Pareto-efficient set.

The study uses the two-objective Deep Sea Treasure environment and the three-objective Mountain Car problem. It examines whether different policy organizations and action-selection mechanisms can recover Pareto solutions across objective dimensionalities. The supplied text confirms the benchmark outcomes but omits training episodes, network details, learning rates, seeds, exact baselines, and a formal ablation table, limiting reproducibility.

In Deep Sea Treasure, the method finds trade-offs between treasure value and time cost. In Mountain Car, it extends to three objectives while still producing effective compromises. These observations support framework generality and modularity, but they do not establish a single percentage improvement. The experiments are primarily feasibility and platform demonstrations rather than a fully quantified competition against standardized baselines.

Potential uses include robotic navigation, autonomous driving with speed-energy-safety trade-offs, resource scheduling, and energy management. Practitioners can define vector rewards and select a linear preference model or a nonlinear Pareto mechanism. Real deployment additionally requires continuous-action algorithms, safety constraints, real-world data, latency analysis, and monitoring under changing preferences.

The evidence comes from two small synthetic benchmarks, largely reflecting discrete-action settings, so real-system performance cannot be inferred directly. The framework offers no new convergence guarantee and does not fully analyze output size, sample complexity, or compute as objective count grows. Future studies should broaden environments and baselines, report hypervolume, coverage, variance, and cost, and address dynamic preferences, constrained control, and continuous actions.