Self-Supervised Policy Adaptation during Deployment (2007.04309v3)

Abstract: In most real world scenarios, a policy trained by reinforcement learning in one environment needs to be deployed in another, potentially quite different environment. However, generalization across different environments is known to be hard. A natural solution would be to keep training after deployment in the new environment, but this cannot be done if the new environment offers no reward signal. Our work explores the use of self-supervision to allow the policy to continue training after deployment without using any rewards. While previous methods explicitly anticipate changes in the new environment, we assume no prior knowledge of those changes yet still obtain significant improvements. Empirical evaluations are performed on diverse simulation environments from DeepMind Control suite and ViZDoom, as well as real robotic manipulation tasks in continuously changing environments, taking observations from an uncalibrated camera. Our method improves generalization in 31 out of 36 environments across various tasks and outperforms domain randomization on a majority of environments.

• The paper presents PAD, a dual-objective framework that integrates RL objectives and self-supervised tasks to adapt policies without rewards.
• It employs inverse dynamics and rotation prediction to refine feature extraction and improve generalization across diverse environments.
• Empirical tests on simulations and real-world robotics demonstrate PAD’s robust and superior performance compared to traditional RL methods.

Analysis of Self-Supervised Policy Adaptation During Deployment

The paper "Self-Supervised Policy Adaptation During Deployment" presents an innovative approach in vision-based reinforcement learning (RL), addressing the critical issue of adapting pre-trained policies to new environments without the need for reward signals. This approach, termed Policy Adaptation during Deployment (PAD), provides a self-supervised learning framework where policies are trained not only with RL objectives but also with auxiliary self-supervision tasks. This dual-objective setup allows the policy to adapt to novel environments in the absence of rewards, which is a significant challenge in real-world applications.

Overview of Methodology

The authors design PAD to operate on top of any policy network and RL algorithm. The network is structured with a shared feature extractor and two heads: one for the RL policy and another for the self-supervised task. During training, the policy network is optimized using both the RL objective and the self-supervised task, which constrains the intermediate feature representations. At deployment, where reward signals are unavailable, the model continues to optimize only the self-supervised task, allowing the feature extractor to adjust to new environmental conditions.

The self-supervised tasks in PAD are crucial for its success. The authors primarily focus on inverse dynamics prediction, where the model predicts the action taken given consecutive image states, and rotation prediction, where the task is to classify the degree of rotation applied to images. These tasks are designed to enhance the generalization capability of the policy to unseen changes in the environment.

Empirical Evaluations

Empirical evaluations demonstrate the efficacy of PAD across a variety of simulation environments and real-world robotic manipulation tasks. The authors show that PAD improves generalization in 31 out of 36 test environments utilizing the DeepMind Control suite and the ViZDoom framework. Particularly in scenarios involving visual changes such as random colors and video backgrounds, PAD significantly outperforms baseline RL strategies, including domain randomization.

The manuscript reports that PAD not only enhances immediate adaptation to new environments but also maintains performance stability over prolonged deployment. Testing on extended episode lengths, PAD exhibited robust performance, indicating that the adaptation does not drift away from the primary RL objective. The researchers also perform successful Sim2Real transfer with a Kinova Gen3 robot, addressing dynamic environmental changes, thus broadening the practical applicability of the proposed method.

Implications for AI and Future Directions

The implications of this research are considerable for enhancing AI adaptability in unpredictable real-world scenarios. By eschewing dependency on reward signals needed for traditional RL fine-tuning, PAD offers a feasible solution for deploying agents in environments with dynamic or unknown characteristics. This capability is critical for autonomous systems, particularly in real-time applications such as robotics and autonomous navigation.

Looking ahead, further research could delve into automating the selection of self-supervised tasks based on the specific RL task, which the authors recognize as a current limitation. Additionally, expanding upon PAD's theoretical framework could provide deeper insights into its adaptability mechanisms and potential integration with other learning paradigms.

In conclusion, this paper contributes significantly to the field of RL by proposing a self-supervised adaptation by which pre-trained policies can efficiently generalize across diverse, unseen environments. The empirical results underscore the practical value and effectiveness of PAD, making this approach promising for future exploration in adaptive AI systems.