Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments (1612.05533v3)

Abstract: In this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task. In particular, we are interested in solutions to this problem that do not require localization, mapping or planning. Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments). To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks. We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances. Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments. We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.

• The paper demonstrates that integrating successor features with deep RL significantly enhances policy transfer for navigation tasks.
• The methodology partitions Q-learning into general features and task-specific dynamics, reducing retraining costs and accelerating learning.
• Real-world tests on the Robotino platform validate the approach by showing faster convergence and robust adaptability compared to classic methods.

Overview of Deep Reinforcement Learning with Successor Features for Navigation

In the presented work, the authors tackle the problem of robotic navigation in simplistic maze-like environments without the reliance on traditional methodologies such as localization, mapping, and planning. The primary focus is to develop a deep reinforcement learning (RL) technique that exploits successor features allowing the system to swiftly adapt to new environments with changed navigation goals. This approach is framed as a series of interrelated reinforcement learning tasks where knowledge transfer from previously completed navigation objectives to new ones reduces the required learning time significantly.

The proposed algorithm demonstrates efficacy by leveraging successor-feature-based deep RL to handle task transfer between sequential tasks. This capability is extensively validated through both simulated environments and real-world robotic implementation involving the Robotino platform. The comparative analysis delineates a method that exhibits superior adaptability in comparison to baseline methods including classical planner-based navigation and contemporary state-of-the-art RL-based methods.

Technical Contributions and Claims

1. Successor Features and Deep RL Integration: The algorithm constructs upon the foundation of using successor features to partition the Q-learning problem into separate components. This separation involves developing general features through deep RL and capturing task dynamics using successor representations.
2. Transfer Learning Mechanism: The established method excels in transferring learned policies across similar navigational tasks. Utilizing successor feature RL, the approach isolates the environment’s dynamics and reward information, facilitating swift adaptation through reconfigurable rewards and policy dynamics.
3. Performance Metrics: Numerical results within simulated experiments show significant improvements in convergence rates for task learning when successor feature-based procedures are employed. These numerical outcomes highlight faster adaptation and retentive policy maintenance across consecutive tasks.
4. Validation Across Modalities: The paper's pragmatic approach situates its evaluation amid diverse settings, showcasing performance using both visual and depth inputs. Furthermore, its execution includes real-world testing environments that strengthen the credibility of proposed techniques beyond rudimentary simulations.

Practical and Theoretical Implications

Theoretically, this work contributes to advancing our understanding of how RL can be structurally decoupled to yield modular transfer capabilities in complex environments. It demonstrates the potential of integrating successor features in deep learning frameworks to circumvent some limitations current RL paradigms face, such as high retraining costs associated with distributional shifts in task landscapes.

Practically, the exploration of successor feature-based RL for navigation offers a blueprint suggesting minimal computational overhead for continuous task learning. The success reflected in real robotic settings indicates future applicability of these methodologies in autonomous systems requiring persistent adaptability, such as autonomous vehicles and robotic assistants.

Future Developments in AI

This research opens several avenues for further exploration:

• Partial Observability Extensions: Employing the algorithm in environments with partial observability could refine its applicability in real-world scenarios demanding incomplete information processing.
• Complex Transfer Dynamics: Extending the approach to handle more complex and dynamic interactions between consecutive tasks could further improve its generalization capabilities.
• Real-world Scaling: Investigations into scaling the methodology to handle larger, more intricate environments can broaden the horizons of deployable AI systems.

In summary, the paper provides insightful contributions towards advancing RL techniques for robotic navigation via the use of successor features, facilitating efficient learning transitions between diverse tasks and environments. Its implications for RL and adaptive robotic applications are both profound and practical, underscoring the necessity for continued research in transferrable learning paradigms.