Uncertainty-Aware Decision Transformer (UNREST) for Offline Reinforcement Learning in Stochastic Driving Environments: Addressing Over-Optimism in Decision Transformers

Concepts de base

Decision Transformers (DTs) struggle in stochastic environments like autonomous driving because they are overly optimistic, assuming actions that succeed once will always succeed. UNREST, a novel uncertainty-aware decision transformer, addresses this by estimating uncertainty and segmenting trajectories to learn from actual decision outcomes rather than unreliable future returns.

Résumé

• Bibliographic Information: Li, Z., Nie, F., Sun, Q., Da, F., & Zhao, H. (2024). Uncertainty-Aware Decision Transformer for Stochastic Driving Environments. In 8th Conference on Robot Learning (CoRL 2024).
• Research Objective: This paper introduces UNREST, a novel approach to adapt Decision Transformers (DTs) for offline reinforcement learning in stochastic environments, particularly focusing on autonomous driving. The authors aim to address the "over-optimism" problem in DTs, where the model incorrectly assumes consistent outcomes for actions in stochastic settings.
• Methodology: UNREST leverages the concept of uncertainty estimation to improve DT's performance. It employs two return transformers to estimate the impact of environmental stochasticity on future returns. Based on this uncertainty measure, UNREST segments driving trajectories into "certain" and "uncertain" parts. In "certain" parts, the model learns from truncated returns less affected by environmental noise, while in "uncertain" parts, it relies on imitating expert actions. This approach allows UNREST to learn from the actual outcomes of decisions rather than unreliable future returns.
• Key Findings: UNREST demonstrates superior performance compared to existing offline RL and imitation learning baselines in various simulated driving scenarios. It achieves higher driving scores, route completion rates, and success rates while maintaining low infraction scores. The authors attribute these improvements to UNREST's ability to effectively handle environmental stochasticity through uncertainty estimation and trajectory segmentation.
• Main Conclusions: UNREST offers a promising solution for offline reinforcement learning in stochastic environments, particularly for complex tasks like autonomous driving. The proposed uncertainty-aware approach effectively addresses the limitations of traditional DTs and paves the way for more reliable and robust offline RL methods.
• Significance: This research significantly contributes to the field of offline reinforcement learning by introducing a novel uncertainty-aware framework for decision transformers. The proposed method addresses a critical challenge in applying DTs to real-world scenarios with inherent stochasticity, making offline RL more applicable to complex control tasks.
• Limitations and Future Research: While UNREST shows promising results, the authors acknowledge limitations in its inference process, which involves multiple auxiliary models and hyperparameters. Future research could explore integrating return and uncertainty predictions into a single model for simplification. Additionally, evaluating UNREST's performance in real-world driving scenarios and investigating its applicability to other domains beyond autonomous driving are promising avenues for future work.

Stats

UNREST achieves a 5.2% absolute driving score improvement in seen scenarios compared to Trajectory Transformer (TT).
UNREST outperforms SPLT by 6.5% in driving score in unseen scenarios.

Citations

"existing works [16, 17, 18] have pointed out that these decision transformers (DTs) tend to be overly optimistic in stochastic environments because they incorrectly assume that actions, which successfully achieve a goal once, can consistently do so in subsequent attempts."
"The key to overcoming the problem is distinguishing between outcomes of decisions and environment transitions, and training models to pursue goals not affected by environmental stochasticity."

Idées clés tirées de

Uncertainty-Aware Decision Transformer for Stochastic Driving Environments

by Zenan Li, Fa... à arxiv.org 10-08-2024

https://arxiv.org/pdf/2309.16397.pdf

Questions plus approfondies

How might UNREST's uncertainty estimation and trajectory segmentation approach be generalized to other application domains beyond autonomous driving, such as robotics or natural language processing?

UNREST's core principles of uncertainty estimation and trajectory segmentation hold significant potential for generalization to other domains beyond autonomous driving. Here's how:
Robotics:

Uncertainty Estimation: In robotics, similar to driving, actions often have stochastic outcomes due to sensor noise, actuator limitations, and unpredictable environment interactions. UNREST's approach of using conditional mutual information between transitions and returns can be adapted to estimate uncertainty in robot manipulation or navigation tasks. For instance, the return could be defined as task success, and the uncertainty model could learn to identify state-action pairs that are highly sensitive to noise or external disturbances.
Trajectory Segmentation: Complex robot tasks can be decomposed into a sequence of sub-tasks. UNREST's segmentation strategy can be applied to divide a robot's trajectory into segments with varying levels of uncertainty. In highly uncertain segments (e.g., grasping an object with unknown properties), the robot could rely on more cautious, reactive control strategies or even switch to learning from demonstrations. In more certain segments (e.g., moving to a known location), the robot could leverage the learned policy to optimize for efficiency.
Natural Language Processing:

Uncertainty Estimation: In tasks like dialogue generation or machine translation, uncertainty arises from ambiguity in language and the open-ended nature of possible responses. UNREST's uncertainty estimation technique could be used to identify uncertain points in a conversation or translation, prompting the model to generate more conservative or diverse outputs. For example, in dialogue, high uncertainty might trigger the model to ask clarifying questions or provide multiple response options.
Trajectory Segmentation: Long sequences of text, such as documents or conversations, can be segmented based on topic shifts or changes in sentiment. UNREST's approach could be adapted to identify these segments and condition language models accordingly. This could lead to more coherent and contextually appropriate text generation.
Key Considerations for Generalization:

Domain-Specific Reward Definition: The definition of "return" needs to be carefully tailored to the specific application domain.
State Representation: Choosing an appropriate state representation that captures the relevant information for uncertainty estimation is crucial.
Action Space: The complexity of the action space will influence the design of the uncertainty estimation and planning modules.

Could incorporating online adaptation or exploration techniques during the deployment of UNREST further enhance its performance in handling novel or unexpected situations in stochastic environments?

Absolutely, incorporating online adaptation or exploration techniques during deployment could significantly enhance UNREST's ability to handle novel or unexpected situations. Here are some potential approaches:

Online Uncertainty Threshold Adaptation: Instead of using a fixed uncertainty threshold (𝜖) during deployment, UNREST could dynamically adjust it based on the observed performance. For example, if the agent encounters many unexpected situations and performs poorly, the threshold could be lowered to encourage more cautious behavior. Conversely, if the agent consistently performs well, the threshold could be raised to allow for more aggressive actions.
Ensemble-Based Exploration: UNREST already uses an ensemble of return prediction models for uncertainty estimation. This ensemble could be further leveraged for exploration by selecting actions based on the model with the highest uncertainty. This would encourage the agent to gather more data in uncertain regions of the state space.
Curiosity-Driven Exploration: Integrating curiosity-driven exploration techniques could incentivize UNREST to actively seek out novel states and actions. This could be achieved by adding a bonus reward term to the agent's objective function that is proportional to the novelty or surprise of the observed transitions.
Online Policy Fine-tuning: If online interaction data becomes available during deployment, UNREST could leverage it to fine-tune its policy. This could be done using techniques like online reinforcement learning or imitation learning, allowing the agent to adapt to changes in the environment or user behavior.
Benefits of Online Adaptation and Exploration:

Improved Generalization: By continuously learning and adapting, UNREST would be better equipped to handle situations not encountered during offline training.
Increased Robustness: Exploration would help identify and mitigate potential failures or edge cases.
Enhanced Safety: In safety-critical applications, online adaptation and exploration could lead to more cautious and reliable decision-making in uncertain situations.

If we consider the ethical implications of AI decision-making in uncertain environments, how can we ensure that methods like UNREST promote safe and responsible behavior, particularly in critical applications like self-driving cars?

Ensuring safe and responsible AI decision-making in uncertain environments, especially in critical applications like self-driving cars, is paramount. Here are key considerations for promoting ethical behavior in methods like UNREST:

Robust Uncertainty Estimation: The foundation of safe behavior lies in accurate and reliable uncertainty estimation. We must:

Develop rigorous evaluation metrics: Go beyond standard benchmarks and design tests that specifically challenge the uncertainty estimation module in diverse, challenging scenarios.
Address biases in training data: Ensure the offline dataset is diverse and representative to avoid biased uncertainty estimates that could lead to unsafe actions in certain situations.

Transparency and Explainability: Understanding why UNREST makes specific decisions is crucial for building trust and accountability.

Develop methods to visualize and interpret: Provide insights into the uncertainty estimation process and how it influences action selection.
Create tools for post-hoc analysis: Enable investigation of critical incidents to identify the root cause and improve the system.

Safety Verification and Validation: Rigorous testing is essential before deployment.

Formal verification techniques: Explore the use of formal methods to mathematically prove the safety properties of UNREST under certain assumptions.
Extensive simulations and closed-course testing: Subject UNREST to a wide range of scenarios, including edge cases and adversarial examples, to identify and address potential vulnerabilities.

Human Oversight and Control: While aiming for autonomy, maintaining a level of human oversight is crucial, especially in the early stages of deployment.

"Human-in-the-loop" systems: Allow human operators to intervene and take control if UNREST encounters situations with high uncertainty or where its decisions seem unsafe.
Clear communication and handover protocols: Establish seamless transitions between autonomous and manual control modes.

Continuous Monitoring and Improvement: Deploying UNREST should not be the final step.

Real-world data collection and analysis: Continuously monitor UNREST's performance in real-world environments, gathering data to identify areas for improvement and address unforeseen challenges.
Regular updates and refinements: Develop a framework for incorporating lessons learned from real-world deployments and feedback from stakeholders to iteratively enhance UNREST's safety and reliability.
By addressing these ethical considerations, we can strive to develop AI systems like UNREST that are not only capable but also trustworthy and responsible, paving the way for their safe and beneficial integration into critical real-world applications.