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Intelligent Switching for Reset-Free RL (2405.01684v1)

Published 2 May 2024 in cs.LG and cs.AI

Abstract: In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resetting} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (\textit{forward}) with learned resets by constructing a second (\textit{backward}) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent's confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL.

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Authors (4)
  1. Darshan Patil (5 papers)
  2. Janarthanan Rajendran (26 papers)
  3. Glen Berseth (48 papers)
  4. Sarath Chandar (93 papers)

Summary

Exploring Intelligent Switching and Bootstrapping in Reset-Free Reinforcement Learning

Introduction to Reset-Free RL Challenges

Reinforcement Learning (RL) has shown remarkable successes in simulated environments. However, transitioning these successes into real-world applications like robotics has been stymied by practical challenges, particularly the need for episodic resets. In reality, unlike simulations, it's not feasible to frequently reset our environment to a desirable initial state. This limitation introduces significant complications since traditional RL relies on these resets to explore state space efficiently and to reattempt tasks from advantageous starting conditions.

To bridge this gap, a new paradigm known as reset-free or autonomous RL has been gaining traction. The core idea here is to enable an RL agent to operate continuously in an environment without resets, learning to revert or "reset" itself to good starting points as needed.

The New Approach: RISC

RISC (Reset-Intelligently Switching Controllers) is a novel algorithm designed to tackle the reset-free RL challenge. It introduces a dual-agent system comprising a forward agent, which learns the primary task, and a backward agent, which learns to reset the initial conditions effectively. Unlike previous methods, RISC doesn't just switch between these agents at fixed intervals or upon goal completion. Instead, it uses a more nuanced approach that depends on the agent's confidence in achieving its current goal.

Key Innovations in RISC

Intelligent Switching: One of RISC's standout features is its intelligent switching mechanism. The decision to switch between the forward and backward agents is determined by a probability proportional to the agent's success in its current direction, assessed through a learned "success critic". This adaptation ensures the agent spends more time learning in parts of the state space where it is less proficient, enhancing overall learning efficiency.

  • Learning When to Switch: Rather than relying on predefined times to switch, RISC uses a dynamic approach based on the agent's proficiency at achieving current goals. This method allows for more flexible and potentially more efficient exploration of the state space.

Advanced Bootstrapping Techniques: RISC also refines how value estimates are updated during transitions—specifically, the last state in a trajectory before a switch. Traditional methods might not bootstrap these last states correctly in reset-free settings, potentially skewing learning. RISC addresses this by consistently bootstrapping the value of the last state, maintaining stable and accurate learning targets irrespective of the agent’s state transitions.

Implementation and Performance

RISC has been tested across several challenging environments designed for reset-free RL, such as robotic manipulation and navigation tasks. The results are impressive, with RISC achieving state-of-the-art performance, suggesting it's better at dealing with the complexities of reset-free RL compared to current methods.

  • Efficient Learning: Not only does RISC handle the lack of resets adeptly, but it also learns significantly faster than other contemporary approaches. This efficiency is crucial in real-world applications where data collection can be time-consuming and costly.

Future Directions

While RISC represents a significant step forward, there's always room for improvement and exploration:

  • Irreversible States: Future versions could focus on handling environments with irreversible states, where an incorrect action by the agent could make it impossible to return to a favorable state.
  • Integration with Demonstrations: Incorporating intelligent mechanisms to leverage demonstrations, similar to some previous works, could further enhance RISC’s learning efficiency and effectiveness.

Conclusion

RISC provides an intriguing solution to some of the key challenges in reset-free RL, leveraging intelligent switching and sophisticated bootstrapping to improve both performance and learning speed. As research progresses, techniques like RISC could pave the way for more robust and autonomous RL applications in real-world settings, beyond the confines of simulated environments.

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