Reinforcement Learning with Imperfect Corrective Actions and Proxy Rewards: An Iterative Approach

แนวคิดหลัก

Combining imperfect proxy rewards with potentially suboptimal human corrective actions in a reinforcement learning framework can lead to more efficient learning and better-aligned policies compared to using either signal alone.

บทคัดย่อ

Bibliographic Information:

Jiang, Z., Feng, X., Zhu, Y., Weng, P., Song, Y., Zhou, T., ... & Fan, C. (2024). REINFORCEMENT LEARNING FROM IMPERFECT CORRECTIVE ACTIONS AND PROXY REWARDS. arXiv preprint arXiv:2410.05782.

Research Objective:

This paper investigates the challenge of training reinforcement learning (RL) agents when a perfect reward function is unavailable. The authors aim to address this by leveraging both imperfect proxy rewards and potentially suboptimal human corrective actions to guide the learning process.

Methodology:

The authors propose ICoPro, an iterative value-based RL algorithm that alternates between three phases: (1) Data Collection: The agent interacts with the environment to collect transition data and receives corrective actions from a human labeler on sampled segments. (2) Finetune: The agent updates its policy using a margin loss to align with the provided corrective actions. (3) Propagation: The agent is trained to maximize expected cumulative proxy rewards while enforcing consistency with both observed and pseudo-labeled corrective actions.

Key Findings:

• ICoPro demonstrates superior performance compared to baselines relying solely on proxy rewards or corrective actions, achieving better alignment with desired behavior and improved sample efficiency.
• The integration of proxy rewards with RL losses in ICoPro proves crucial for achieving better alignment than using corrective actions alone.
• ICoPro exhibits robustness to different levels of imperfection in both proxy rewards and corrective actions, effectively leveraging the complementary nature of the two signals.

Main Conclusions:

The study highlights the effectiveness of combining imperfect proxy rewards and human corrective actions for training RL agents. The proposed ICoPro algorithm successfully leverages both signals to achieve better-aligned policies and improved sample efficiency compared to using either signal in isolation.

Significance:

This research contributes to the field of RLHF by proposing a practical framework for incorporating both imperfect reward signals and human feedback. The findings have implications for developing more robust and aligned RL agents in real-world applications where defining perfect reward functions is challenging.

Limitations and Future Research:

• The current implementation uses uniform query selection for human feedback, which could be further optimized using more sophisticated sampling methods.
• Future work could explore theoretical guarantees for the synergistic combination of imperfect proxy rewards and suboptimal corrective actions under specific assumptions.

สถิติ

Only at most 1% of the environmental transitions are labeled in the experiments.

คำพูด

ข้อมูลเชิงลึกที่สำคัญจาก

Reinforcement Learning From Imperfect Corrective Actions And Proxy Rewards

by Zhaohui Jian... ที่ arxiv.org 10-10-2024

https://arxiv.org/pdf/2410.05782.pdf

สอบถามเพิ่มเติม

How can the selection of query segments for human feedback be optimized to further reduce labeling effort without compromising performance?

In the context of the ICoPro algorithm, optimizing the selection of query segments for human feedback is crucial for minimizing labeling effort while maximizing learning efficiency. Here are several strategies that could be employed:
1. Uncertainty-Based Sampling:

Action-Value Uncertainty: Prioritize segments where the agent's Q-values exhibit high variance or entropy across actions. This indicates states where the agent is uncertain about the optimal action, making human feedback most valuable.
Value Function Disagreement: If using an ensemble of Q-networks, prioritize segments where the Q-value estimates from different ensemble members disagree significantly. This signals states where the agent's understanding of the environment is inconsistent, highlighting areas for improvement through human feedback.
2. State-Based Importance Sampling:

Novelty Detection: Prioritize segments containing states that are significantly different from those previously labeled. This encourages exploration and prevents overfitting to a limited set of states. Techniques like density estimation or distance metrics in a learned state embedding space can be used to identify novel states.
Reachability Estimation: Prioritize segments that lead to important or rarely visited states. This ensures that the agent receives feedback on critical parts of the state space, even if they are not frequently encountered during random exploration.
3. Performance-Based Sampling:

High-Impact Actions: Prioritize segments where the agent's actions have a significant impact on the cumulative reward or task completion. This focuses human feedback on actions that are most crucial for achieving the desired outcome.
Error Analysis: Analyze the agent's performance and identify specific types of errors or suboptimal behaviors. Design query selection strategies to target these specific areas for improvement through human feedback.
4. Active Learning Techniques:

Expected Value of Information (EVOI): Formally quantify the expected improvement in the agent's policy from labeling a particular segment. This requires modeling the relationship between human feedback and policy improvement, which can be challenging but potentially very effective.
Query by Committee (QBC): If using an ensemble of agents, prioritize segments where the agents disagree most on the optimal action. This focuses human feedback on areas where the current policy is most uncertain.
By strategically selecting query segments based on these criteria, we can ensure that human feedback is directed towards the most informative and impactful parts of the state space, leading to more efficient learning and reduced labeling effort.

Under what specific conditions or assumptions can we theoretically guarantee the effectiveness of combining imperfect proxy rewards with suboptimal corrective actions?

While empirically demonstrating the effectiveness of combining imperfect signals is valuable, establishing theoretical guarantees requires specific conditions and assumptions. Here are some potential avenues for analysis:
1. Bounded Suboptimality:

Assumption: Assume that both the proxy reward function and the human labeler's policy have bounded suboptimality. This means their performance deviates from the optimal policy by a limited margin.
Potential Guarantee: Under this assumption, it might be possible to derive bounds on the suboptimality of the learned policy in ICoPro. The bounds would depend on the degree of suboptimality of the proxy rewards and corrective actions, as well as the learning algorithm's properties.
2. Complementary Information:

Assumption: Assume that the proxy rewards and corrective actions provide complementary information about the optimal policy. This means they are informative in different parts of the state-action space.
Potential Guarantee: If the imperfections of the two signals are uncorrelated or negatively correlated, their combination can lead to a more accurate estimate of the optimal policy than either signal alone. This could be formalized by analyzing the reduction in uncertainty or bias of the learned policy when combining both signals.
3. Structure in Suboptimality:

Assumption: Assume that the suboptimality of the proxy rewards and corrective actions exhibits some structure. For example, they might be consistently biased towards certain actions or states.
Potential Guarantee: If the structure of suboptimality is identifiable, the learning algorithm could potentially leverage this information to compensate for the imperfections. This might involve learning a mapping from the observed signals to a more accurate representation of the true reward function.
4. Regularization Effect:

Assumption: Assume that the corrective actions act as a form of regularization on the policy learned from the proxy rewards. This means they prevent the policy from overfitting to the potentially misleading information in the proxy rewards.
Potential Guarantee: This regularization effect could be analyzed by studying the generalization properties of the learned policy. It might be possible to show that combining corrective actions leads to a policy that performs better on unseen states or under slight variations in the environment.
Challenges and Future Work:

Deriving formal guarantees for the effectiveness of combining imperfect signals in RL is challenging due to the complex interplay between exploration, exploitation, and generalization.
Future work could focus on developing theoretical frameworks that explicitly account for the specific types of imperfections present in the proxy rewards and corrective actions.
Additionally, analyzing the sample complexity of learning under these conditions would provide insights into the data efficiency of combining imperfect signals.

Could this framework be extended to incorporate other forms of human feedback, such as natural language instructions or demonstrations, to further enhance learning and alignment?

Yes, the ICoPro framework can be extended to incorporate other forms of human feedback, enriching the learning process and improving alignment with human preferences. Here are some potential extensions:
1. Incorporating Natural Language Instructions:

Reward Shaping: Natural language instructions can be used to define auxiliary reward functions that guide the agent towards desired behaviors. For example, instructions like "avoid obstacles" or "reach the goal quickly" can be translated into penalties for collisions or bonuses for shorter episode lengths.
State-Action Constraints: Instructions can be interpreted as constraints on the agent's actions in specific states. For example, "don't cross the red line" could be enforced by masking out actions that would lead to crossing the line.
Hierarchical Planning: High-level instructions can be used to decompose complex tasks into simpler subtasks, enabling hierarchical planning and learning. The agent can then receive feedback at different levels of granularity, improving its understanding of both the overall goal and the individual steps required to achieve it.
2. Integrating Demonstrations:

Initialization: Demonstrations can be used to pre-train the agent's policy or value function, providing a strong starting point for learning. This can significantly reduce the number of interactions required for the agent to achieve reasonable performance.
Imitation Learning: Demonstrations can be incorporated into the training process through imitation learning losses, encouraging the agent to mimic the expert's behavior. This can be particularly useful for learning complex manipulation or navigation tasks.
Curriculum Learning: Demonstrations can be used to create a curriculum of increasingly difficult tasks, gradually increasing the agent's capabilities. This can help prevent the agent from getting stuck in local optima and facilitate learning of more complex behaviors.
3. Combining Multiple Feedback Modalities:

Multimodal Learning: The ICoPro framework can be extended to handle multiple feedback modalities simultaneously, leveraging the strengths of each type of feedback. For example, demonstrations can provide a global understanding of the task, while corrective actions can fine-tune the agent's behavior in specific situations.
Adaptive Feedback Selection: The system can learn to adaptively select the most informative type of feedback based on the agent's current state and performance. This can further reduce the burden on human labelers by requesting only the most valuable feedback at each stage of learning.
Benefits of Incorporating Diverse Feedback:

Improved Sample Efficiency: Combining multiple feedback modalities can significantly reduce the number of interactions and human labels required for the agent to learn effectively.
Enhanced Alignment: Incorporating diverse forms of human feedback allows for a more nuanced and comprehensive expression of human preferences, leading to better alignment between the agent's behavior and the desired outcome.
Increased Task Complexity: Extending the framework to handle richer feedback modalities enables the learning of more complex tasks that would be difficult or impossible to specify using only rewards or corrective actions.