The paper aims to address the problem of aligning robot behavior with human preferences efficiently through interactive imitation learning from human intervention . This problem involves inferring a residual reward function that captures the difference between the human expert's internal reward function and that of the prior policy, and then using Residual Q-Learning (RQL) to adjust the policy accordingly . While the concept of aligning robot behavior with human preferences is not new, the approach proposed in the paper, MEREQ (Maximum-Entropy Residual-Q Inverse Reinforcement Learning), introduces a novel method to improve sample efficiency by focusing on learning the residual reward function rather than inferring the full human reward function from interventions .

This paper seeks to validate the scientific hypothesis that MEREQ (Maximum-Entropy Residual-Q Inverse Reinforcement Learning) can efficiently align robot behavior with human preferences through sample-efficient policy alignment from human intervention . The key idea behind MEREQ is to infer a residual reward function that captures the difference between the human expert's reward function and the prior policy's reward function, enabling the alignment of the policy with human preferences using Residual Q-Learning (RQL) . The study aims to demonstrate that MEREQ can effectively leverage the prior policy to reduce the number of expert intervention samples required for alignment, thus enhancing sample efficiency in learning from human intervention .

The paper proposes a novel method called MEREQ (Maximum-Entropy Residual-Q Inverse Reinforcement Learning) for sample-efficient alignment from human intervention. This method aims to better align the prior policy with human preferences in a more efficient manner . MEREQ focuses on learning a residual reward through Inverse Reinforcement Learning (IRL) to align the policy with the unknown expert reward, which leads to improved sample efficiency .

One key insight behind MEREQ is to infer a residual reward function that captures the discrepancy between the human expert's internal reward function and that of the prior policy. This approach differs from inferring the full human reward function from interventions, making it more efficient .

MEREQ utilizes Residual Q-Learning (RQL) to fine-tune and align the policy with the unknown expert reward. This process only requires learning the residual weights from expert trajectories without knowing the full reward function, making it more sample-efficient compared to traditional methods like MaxEnt .

The paper also introduces the concept of policy customization, where the goal is to find a new policy that optimizes the task objective of the prior policy and additional task objectives specified by a downstream task. RQL is proposed as an initial solution for policy customization, which involves finding a max-ent policy for a new Markov Decision Process (MDP) defined by a residual reward function that quantifies the discrepancy between the original task objective and the customized task objective .

Overall, the paper presents innovative ideas and methods such as MEREQ, residual reward inference, and policy customization to address the challenge of aligning the prior policy with human preferences efficiently through sample-efficient learning from human intervention . The proposed method, MEREQ (Maximum-Entropy Residual-Q Inverse Reinforcement Learning), offers several key characteristics and advantages compared to previous methods outlined in the paper .

1. Residual Reward Inference: MEREQ introduces a novel approach to infer a residual reward function that captures the difference between the human expert's internal reward function and that of the prior policy. This method focuses on learning the residual reward through Inverse Reinforcement Learning (IRL) to align the policy with human preferences efficiently .

2. Sample-Efficient Alignment: MEREQ aims to achieve sample-efficient alignment from human intervention by leveraging Residual Q-Learning (RQL) to fine-tune and align the policy with the unknown expert reward. This approach requires learning the residual weights from expert trajectories without knowing the full reward function, leading to improved sample efficiency compared to traditional methods like MaxEnt .

3. Policy Customization: The paper introduces the concept of policy customization, where MEREQ focuses on finding a new policy that optimizes the task objective of the prior policy and additional task objectives specified by a downstream task. RQL is proposed as an initial solution for policy customization, enabling the alignment of the policy with customized task objectives efficiently .

4. Efficient Learning from Interventions: Unlike behavior cloning (BC) methods that ignore the sequential nature of decision-making, MEREQ within the IRL framework accounts for the sequential nature of human decision-making and transition dynamics. This approach is more effective for fine-tuning settings and avoids catastrophic forgetting, enhancing sample efficiency in learning from human interventions .

5. Direct Inference of Residual Weights: MEREQ introduces a method to directly infer the residual weights from expert trajectories without knowing the full reward function. By applying RQL with the inferred residual weights, the policy can be updated more efficiently, reducing the number of expert intervention samples needed for alignment .

Overall, MEREQ stands out for its innovative approach to residual reward inference, sample-efficient alignment, policy customization, and direct inference of residual weights, offering significant advantages in efficiently aligning the prior policy with human preferences through learning from human intervention .

Several related research studies exist in the field of interactive imitation learning and inverse reinforcement learning. Noteworthy researchers in this field include:

• A. Jain, B. Wojcik, T. Joachims, and A. Saxena
• P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei
• E. Bıyık, D. P. Losey, M. Palan, N. C. Landolfi, G. Shevchuk, and D. Sadigh
• K. Lee, L. Smith, and P. Abbeel
• X. Wang, K. Lee, K. Hakhamaneshi, P. Abbeel, and M. Laskin

The key to the solution mentioned in the paper "MEREQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention" is the introduction of MEREQ (Maximum-Entropy Residual-Q Inverse Reinforcement Learning). This method is designed for sample-efficient alignment from human intervention by inferring a residual reward function that captures the discrepancy between the human expert's preferences and the prior policy's reward functions. It then utilizes Residual Q-Learning (RQL) to align the policy with human preferences using this residual reward function, achieving sample-efficient policy alignment from human intervention .

The experiments in the paper were designed to evaluate the effectiveness of the proposed method, MEREQ, for sample-efficient alignment from human intervention . The experiments involved simulated and real-world tasks categorized based on the type of expert involved . The tasks aimed to align robot behavior with human preferences through interactive imitation learning using human interventions as feedback . The experiments focused on learning from human intervention within the inverse reinforcement learning (IRL) framework, which models the expert as a sequential decision-making agent and infers the expert's reward function from demonstrations . The key insight behind MEREQ was to infer a residual reward function that captures the discrepancy between the human expert's internal reward function and that of the prior policy, enabling sample-efficient policy alignment from human intervention .

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context . Regarding the availability of the code as open source, the information about the code being open source is not provided in the context as well.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified. The paper introduces MEREQ (Maximum-Entropy Residual-Q Inverse Reinforcement Learning) as a method designed for sample-efficient alignment from human intervention . The experiments conducted on simulated and real-world tasks demonstrate that MEREQ achieves sample-efficient policy alignment from human intervention . The results show that MEREQ and its variation MEReQ-NP require fewer total expert samples to achieve comparable policy performance compared to other baselines under varying criteria strengths in different tasks and environments . This indicates the effectiveness of MEREQ in leveraging human interventions for policy alignment while maintaining sample efficiency.

Furthermore, the paper discusses the limitations of existing methods in efficiently utilizing prior policies to facilitate learning from human interventions . By introducing MEREQ, which infers a residual reward function capturing the discrepancy between human expert preferences and prior policy rewards, the paper addresses the challenge of aligning policies with human preferences effectively . The use of Residual Q-Learning (RQL) in MEREQ allows for fine-tuning and aligning policies with unknown expert rewards, leading to improved sample efficiency in policy alignment .

Overall, the experiments and results presented in the paper provide a comprehensive analysis of the effectiveness of MEREQ in achieving sample-efficient alignment from human intervention. By addressing the limitations of existing methods and introducing a novel approach that leverages residual reward functions, the paper contributes significantly to the field of interactive imitation learning and human-in-the-loop machine learning .

The paper "MEREQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention" makes the following contributions:

• Introduces MEREQ (Maximum-Entropy Residual-Q Inverse Reinforcement Learning) for sample-efficient alignment from human intervention, focusing on aligning robot behavior with human preferences through interactive imitation learning .
• Proposes inferring a residual reward function to capture the discrepancy between the human expert's preferences and the prior policy's reward functions, utilizing Residual Q-Learning (RQL) to align the policy with human preferences effectively .
• Conducts extensive evaluations on simulated and real-world tasks to demonstrate that MEREQ achieves sample-efficient policy alignment from human intervention, showcasing its effectiveness in aligning robot behavior with human preferences .

To delve deeper into the topic, further exploration can be conducted on the following aspects:

• Policy Customization and Residual Q-Learning: Investigating the application of Residual Q-Learning (RQL) in policy customization, where a new policy is optimized to achieve both the original task objective and additional objectives specified by a downstream task .
• Learning from Intervention: Exploring the effectiveness of learning from intervention approaches, such as MEREQ (Maximum-Entropy Residual-Q Inverse Reinforcement Learning), in aligning prior policies with human preferences efficiently through residual reward learning .
• Human-in-the-Loop Experiments: Conducting further studies on human-in-the-loop experiments to understand how human experts can intervene, control, and engage with the learning process, particularly in tasks like Highway-Human and Bottle-Pushing-Human .
• Sample-Efficient Alignment: Analyzing the sample efficiency of different approaches, including MEReQ, MEReQ-NP, MaxEnt-FT, and MaxEnt, to determine the number of expert samples required for policy alignment and intervention rate .
• Expert Intervention Learning: Exploring frameworks for robot learning from explicit and implicit human feedback, such as expert intervention learning, to enhance the learning process and adapt to human preferences .
• Interactive Reinforcement Learning: Investigating the principles and challenges of interactive reinforcement learning, including the design aspects and outcomes of human-in-the-loop machine learning interactions .
• Reward Function Learning: Studying methods for learning reward functions from diverse sources of human feedback to optimize the learning process and improve policy alignment with human preferences .
• Fine-Tuning and Alignment: Examining the efficiency of fine-tuning prior policies from human interventions to reduce the number of expert intervention samples needed for alignment, as discussed in the context .