Generalization in Transfer Learning

Deep reinforcement learning (Deep RL) has revolutionized control tasks, with algorithms like TRPO, PPO, and SAC achieving human-level performance in simulated environments. However, these successes are often confined to training conditions, revealing a critical challenge: generalization. Early studies focused on optimizing performance within a fixed environment, but real-world applications demand policies that adapt to unseen scenarios. Techniques such as domain randomization, adversarial training, and meta-learning have been explored to enhance robustness, yet none fully address the overfitting problem or extend transfer ranges sufficiently. Recent efforts incorporate regularization and ensemble methods, but their effectiveness diminishes as environmental shifts grow more complex. This paper builds on these foundations, proposing a multi-faceted regularization framework that systematically improves transferability and robustness in continuous control tasks.

Despite advances in deep RL, models tend to overfit source environments, limiting their ability to generalize to new, unseen conditions. This overfitting manifests as poor transfer performance when environmental parameters such as gravity, friction, or robot morphology change significantly. Existing methods like domain randomization and adversarial training improve robustness to some extent but often lack systematic regularization strategies to prevent overfitting. Moreover, hyperparameters like training iteration count are rarely treated as explicit variables, leading to inconsistent transfer performance. The core challenge is designing a training framework that inherently promotes policy robustness across a broad spectrum of environmental variations, ensuring reliable deployment in real-world scenarios.

The paper introduces several key innovations: 1) treating training iteration as a hyperparameter, enabling early stopping to prevent overfitting; 2) developing strict clipping (SC-PPO) that discards samples causing overfitting, thus regularizing policy updates; 3) integrating maximum entropy regularization to enhance environment domain randomization, increasing the diversity of training conditions; 4) employing multiple critic networks with averaged advantage estimates (ACC-RARL) to reduce overfitting in value functions; and 5) combining curriculum learning with adversarial training to progressively expose the policy to harder environments. These strategies collectively create a robust, scalable framework for transfer learning in continuous control tasks, significantly broadening the transfer success range and improving policy stability.

• �� Multiple policies are trained with varying hyperparameters, especially different training iteration counts, and snapshots are stored in a policy buffer.
• �� Early stopping is employed by evaluating policy performance on proxy validation tasks, selecting snapshots that balance source task performance and transferability.
• �� Strict clipping (SC-PPO) reduces the clipping hyperparameter, constraining policy updates to prevent overfitting.
• �� Max entropy regularization is added to the reward functions, promoting exploration and environment variability.
• �� Multiple critic networks are used, with their outputs averaged to estimate advantages more robustly (ACC-RARL).
• �� Curriculum learning dynamically adjusts adversarial difficulty, exposing the policy to increasingly challenging environments.
• �� The entire framework is trained and validated in MuJoCo simulation environments, with systematic ablation studies to evaluate each component's contribution.

The experiments involve transfer tasks in MuJoCo Humanoid and Hopper environments, where environment parameters such as gravity, mass, and friction are systematically varied. Baselines include standard PPO, RARL, and unregularized models. Hyperparameters like clipping thresholds, number of critics, and curriculum schedules are tuned via grid search. Transfer success is measured by average reward and success rate over multiple random seeds. The evaluation spans a broad range of environment shifts, with ablation studies isolating each regularization component. Results demonstrate that the proposed framework extends transfer ranges and enhances robustness, with statistical significance confirmed through multiple runs.

The proposed methods outperform baselines across all transfer tasks. For example, in Hopper, the transfer range for Torso Mass increased from 1-4 (baseline) to 1-6 with SC-PPO, with reward improvements of 15%. In Humanoid, the transfer success rate under varied gravity conditions improved by 20%. Max entropy regularization led to a 20% increase in robustness against friction coefficient variations. Early stopping prevented overfitting, maintaining high performance across environment shifts. Combining multiple critics and curriculum learning further stabilized policies, enabling successful transfer in more extreme environment changes than previous methods.

This framework can be directly applied to robotic control systems requiring adaptation to diverse terrains, payloads, or environmental conditions. It is suitable for autonomous vehicles, industrial robots, and service robots operating in unpredictable real-world settings. The approach enhances safety and reliability by producing policies that generalize well, reducing the need for environment-specific retraining. Long-term, it paves the way for scalable, autonomous systems capable of lifelong learning and adaptation in complex environments, with potential integration into real-time control systems and multi-task learning architectures.

While effective, the approach relies on extensive hyperparameter tuning, especially for regularization parameters, which may limit real-world scalability. Computational overhead increases with multiple critics and adversarial components, posing challenges for deployment on resource-constrained platforms. The methods have been validated primarily in simulation; transferring to real robots involves sim-to-real gaps that require further investigation. Additionally, extreme environmental shifts beyond training distributions may still cause performance degradation, necessitating adaptive or online regularization strategies.