Trial without Error: Towards Safe Reinforcement Learning via Human Intervention (1707.05173v1)

Abstract: AI systems are increasingly applied to complex tasks that involve interaction with humans. During training, such systems are potentially dangerous, as they haven't yet learned to avoid actions that could cause serious harm. How can an AI system explore and learn without making a single mistake that harms humans or otherwise causes serious damage? For model-free reinforcement learning, having a human "in the loop" and ready to intervene is currently the only way to prevent all catastrophes. We formalize human intervention for RL and show how to reduce the human labor required by training a supervised learner to imitate the human's intervention decisions. We evaluate this scheme on Atari games, with a Deep RL agent being overseen by a human for four hours. When the class of catastrophes is simple, we are able to prevent all catastrophes without affecting the agent's learning (whereas an RL baseline fails due to catastrophic forgetting). However, this scheme is less successful when catastrophes are more complex: it reduces but does not eliminate catastrophes and the supervised learner fails on adversarial examples found by the agent. Extrapolating to more challenging environments, we show that our implementation would not scale (due to the infeasible amount of human labor required). We outline extensions of the scheme that are necessary if we are to train model-free agents without a single catastrophe.

Summary of "Trial without Error: Towards Safe Reinforcement Learning via Human Intervention"

The paper "Trial without Error: Towards Safe Reinforcement Learning via Human Intervention" presents a framework for ensuring safety in model-free reinforcement learning (RL) by incorporating human oversight during the training phase. The authors propose a Human Intervention Reinforcement Learning (HIRL) scheme designed to prevent RL agents from taking catastrophic actions during exploration by utilizing human supervisors and training supervised learners to mimic human intervention policies.

Key Contributions

The paper makes several significant contributions in the field of safe RL:

1. Formalizing Human Intervention: The authors introduce HIRL, a formal scheme that involves active human oversight during the initial training phase of an RL agent. Human supervisors block potentially catastrophic actions in real time, thereby preventing any negative outcomes that could occur from trial-and-error learning.
2. Supervised Learner Training: The human intervention data collected during training is utilized to train a supervised learner — termed "Blocker" — that emulates the decisions of the human supervisor. This Blocker can subsequently replace human oversight and maintain safety against catastrophic actions in future training or deployment phases.
3. Evaluation and Results: The proposed HIRL framework was empirically tested on Atari games, including Pong, Space Invaders, and Road Runner. The results demonstrated varying degrees of success; in simpler scenarios, the Blocker managed to entirely prevent catastrophes while still enabling effective learning. However, with more complex catastrophic scenarios, the system fell short, highlighting its limitations and challenges.

Numerical and Experimental Findings

The empirical evaluation of the HIRL scheme provided insightful results:

• In Pong and Space Invaders, the Blocker achieved its goal of zero catastrophes without hindering the agent’s learning process.
• In Road Runner, HIRL reduced the rate of catastrophes significantly but did not completely eliminate them, primarily due to adversarial examples created by the agent’s exploration.
• Comparison with a baseline approach, where catastrophic actions are punished instead of being blocked, showcased HIRL's superior capability in mitigating catastrophic forgetting — a major issue in RL.

Challenges and Implications

The paper outlines several challenges intrinsic to the HIRL scheme:

• Scalability: Scaling the proposed solution to more complex environments requires an infeasible amount of human intervention.
• Adversarial Examples: The supervised learners faced difficulties in robustly identifying adversarial attack scenarios generated by the RL agents, indicating a need for more sophisticated training approaches.
• Human Labor: The potential human labor involved in supervising an RL system could be immense, particularly for complex real-world domains or sophisticated environments such as advanced video games.

Future Directions

Looking forward, the authors propose several strategies to address the current limitations of HIRL:

• Improving the data efficiency of Blockers to reduce human labor without increasing risk exposure.
• Developing model-based RL methods for predictive safety mechanisms, potentially eliminating the need for hands-on intervention during risk scenarios.
• Implementing active learning techniques to have systems strategically request human oversight only when uncertain about the safety of their actions.
• Exploring transfer learning and simulation to extend learned safeguards to varied environments and tasks, thus reducing the necessity for repetitive human interventions.

In conclusion, the HIRL framework represents a significant step towards safer RL practices by formalizing human oversight and deploying supervised learners for intervention tasks. However, realizing scalable and robust implementations in varied domains will depend on overcoming substantial technical and practical challenges. Future research focusing on these aspects could pave the way for the wider adoption of safe RL systems in real-world applications.