Loss is its own Reward: Self-Supervision for Reinforcement Learning (1612.07307v2)

Abstract: Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of self-supervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquitous and instantaneous supervision for representation learning even in the absence of reward. While current results show that learning from reward alone is feasible, pure reinforcement learning methods are constrained by computational and data efficiency issues that can be remedied by auxiliary losses. Self-supervised pre-training and joint optimization improve the data efficiency and policy returns of end-to-end reinforcement learning.

• The paper introduces self-supervised auxiliary tasks—reward prediction, inverse dynamics, and dynamics verification—to enrich the RL training process.
• It combines joint optimization with self-supervised pre-training to significantly improve data efficiency and accelerate policy convergence.
• Empirical results demonstrate a 1.4× improvement in achieving near-optimal policy returns, underscoring the practical benefits of self-supervision in RL.

Self-Supervision for Reinforcement Learning: An Analytical Overview

The paper "Loss is its own Reward: Self-Supervision for Reinforcement Learning" proposes a novel approach to enhancing the efficacy of reinforcement learning (RL) through self-supervised auxiliary losses. This work explores how self-supervised tasks, derived from unlabeled data, can be used to improve the data efficiency and policy returns of RL systems. The central thesis of the paper posits that auxiliary losses serve as additional sources of supervision, thus enriching the learning process beyond what is typically achieved with sparse and delayed rewards.

Key Contributions

The authors present several self-supervised tasks, aptly chosen for their diversity and compatibility with the data structures intrinsic to RL environments. These tasks include:

1. Reward Prediction: Utilizing the reward signal in a self-supervised context to predict instantaneous rewards, effectively reducing noise associated with policy stochasticity.
2. Dynamics Verification: A classification-based task to verify whether state-successor pairs originate from the environment, capturing critical dynamics within state transitions.
3. Inverse Dynamics: Classification of action sequences from state transitions, which is particularly advantageous when the action space is significantly smaller than the state space.
4. Reconstruction: Traditional auto-encoding methodologies targeting reconstruction of input data as a means of representation learning; however, the authors find this less effective compared to the other auxiliary tasks.

The paper also details a methodology that combines policy optimization with these self-supervised objectives, termed joint optimization, ensuring that auxiliary losses are conditioned on the policy distribution throughout training.

Empirical Findings

The research notes a considerable improvement in data efficiency and policy performance with self-supervised pre-training and joint optimization. Notable findings include:

• A marked increase in data efficiency, with multi-task self-supervision leading to an average $1.4\times$ improvement to reach $95\%$ of the best achieved policy returns.
• Joint optimization surpassing the benefits of pre-training alone, thus demonstrating the effectiveness of self-supervised losses as a continuous part of the training process.

Implications and Future Directions

This work significantly extends the theoretical understanding of how auxiliary signals derived from the environment can accelerate and enhance RL processes. Practically, these findings suggest that integrating self-supervised learning into RL architectures can mitigate the constraints associated with sparse reward signals, thus leading to more robust and efficient learning systems.

From a theoretical perspective, this strategy broadens the RL optimization landscape by considering extrinsic and intrinsic rewards. Such comprehensive learning frameworks hold promise for more generalized learning approaches in AI systems, potentially impacting applications ranging from robotics to game playing.

Future research could explore the integration of intrinsic rewards derived from self-supervised losses, effectively guiding exploration by favoring transitions that maximize learning efficiency. Furthermore, scaling these methods to more complex environments and assessing the interplay between various types of self-supervised tasks may yield deeper insights into optimizing representation learning for RL.

Conclusion

The paper makes a compelling argument for the inclusion of self-supervised auxiliary losses in reinforcement learning, demonstrating their ability to improve both the speed and quality of policy convergence. Through rigorous empirical analysis, the authors successfully validate their claims, offering a solid foundation for future explorations into more advanced, self-supervised RL systems.