Feeza Khan Khanzada

Feeza Khan Khanzada, Jaerock Kwon

Learned driving agents often degrade when deployed in unseen environments. This paper studies a deliberately bounded instance of that problem in the CARLA simulator: zero-shot transfer of a closed-loop fixed-route driving agent from Town05 and Town06 to unseen Town03 and Town04. The study isolates structural town shift by keeping weather fixed to ClearNoon and removing traffic and pedestrians. We build on a Dreamer-style latent world-model agent and add two training-only auxiliary losses: multi-horizon prediction of future visual-semantic embeddings along imagined rollouts and town-adversarial supervision on a semantic projection of the recurrent latent state. A causal context feature conditions the semantic rollout predictor, while the actor and critic retain the standard control feature. The policy receives no navigation command, route polyline, goal pose, or map input; the reference route is used only by the environment for reward, progress, success, and termination. Across the evaluated held-out towns, the proposed model achieves the highest mean success rate among the included Dreamer-family methods. Secondary safety and lane-keeping metrics are mixed across towns. These results support a bounded conclusion: in this controlled fixed-weather CARLA setting, semantic rollout supervision combined with town-adversarial regularization improves mean held-out-town route completion.

Elahe Delavari, Feeza Khan Khanzada, Jaerock Kwon

Reinforcement Learning (RL) offers a promising framework for autonomous driving by enabling agents to learn control policies through interaction with environments. However, large and high-dimensional action spaces often used to support fine-grained control can impede training efficiency and increase exploration costs. In this study, we introduce and evaluate two novel structured action space modification strategies for RL in autonomous driving: dynamic masking and relative action space reduction. These approaches are systematically compared against fixed reduction schemes and full action space baselines to assess their impact on policy learning and performance. Our framework leverages a multimodal Proximal Policy Optimization agent that processes both semantic image sequences and scalar vehicle states. The proposed dynamic and relative strategies incorporate real-time action masking based on context and state transitions, preserving action consistency while eliminating invalid or suboptimal choices. Through comprehensive experiments across diverse driving routes, we show that action space reduction significantly improves training stability and policy performance. The dynamic and relative schemes, in particular, achieve a favorable balance between learning speed, control precision, and generalization. These findings highlight the importance of context-aware action space design for scalable and reliable RL in autonomous driving tasks.

Feeza Khan Khanzada, Jaerock Kwon

Model-based Reinforcement Learning (MBRL) has emerged as a promising paradigm for autonomous driving, where data efficiency and robustness are critical. Yet, existing solutions often rely on carefully crafted, task specific extrinsic rewards, limiting generalization to new tasks or environments. In this paper, we propose InDRiVE (Intrinsic Disagreement based Reinforcement for Vehicle Exploration), a method that leverages purely intrinsic, disagreement based rewards within a Dreamer based MBRL framework. By training an ensemble of world models, the agent actively explores high uncertainty regions of environments without any task specific feedback. This approach yields a task agnostic latent representation, allowing for rapid zero shot or few shot fine tuning on downstream driving tasks such as lane following and collision avoidance. Experimental results in both seen and unseen environments demonstrate that InDRiVE achieves higher success rates and fewer infractions compared to DreamerV2 and DreamerV3 baselines despite using significantly fewer training steps. Our findings highlight the effectiveness of purely intrinsic exploration for learning robust vehicle control behaviors, paving the way for more scalable and adaptable autonomous driving systems.