Uncertainty in reinforcement learning
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Summary
10 papers analyzed
These studies suggest that incorporating and quantifying uncertainty in reinforcement learning improves decision-making, exploration, risk sensitivity, and performance in uncertain environments.
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Beta20 papers analyzed
Introduction
Uncertainty in reinforcement learning (RL) is a critical aspect that affects the performance and reliability of RL algorithms. It arises from various sources, including noisy environments, limited data, and the inherent randomness in the system dynamics. Addressing uncertainty is essential for developing robust and efficient RL models, especially in high-stakes applications such as healthcare, autonomous driving, and financial decision-making.
Key Insights
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State Uncertainty and Reward Allocation:
- Ignoring state uncertainty leads to incorrect value estimates. Posterior weighted reinforcement learning updates state probabilities based on observed rewards, improving accuracy.
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Uncertainty Quantification for Exploration:
- Quantifying uncertainty helps in better decision-making and exploration. Large-sample asymptotic distributions for Q-values enable the evaluation of decision assertiveness and facilitate the development of exploration policies that maximize learning efficiency.
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Disentangling Epistemic and Aleatoric Uncertainty:
- Separating epistemic (data-related) and aleatoric (inherent randomness) uncertainties is crucial for risk-sensitive algorithms. Combining distributional RL and Bayesian inference helps in effectively managing both types of uncertainty .
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Modeling System Dynamics Uncertainty:
- Representing uncertainty in state dynamics as a measure on the space of functions allows for continuous updates based on new information, aiding in the exploration-exploitation tradeoff .
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Deep Exploration in Model-Based RL:
- Incorporating epistemic uncertainty into planning trees enhances deep exploration and sample efficiency. This approach stabilizes learning from exploratory trajectories.
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Risk-Sensitive Planning:
- Using uncertainty-guided planning methods, such as cross-entropy method planning, helps in identifying and avoiding high-uncertainty regions, promoting safer and more trustworthy RL agents.
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Uncertainty-Foreseeing Models:
- Proactively foreseeing uncertainty before generating model trajectories improves the quality of samples and stabilizes learning targets. This approach aligns model learning objectives with their usage in multi-step predictions.
Conclusion
Addressing uncertainty in reinforcement learning is pivotal for developing robust and efficient algorithms. Key strategies include posterior weighted reinforcement learning for state uncertainty, quantifying uncertainty for better exploration, disentangling epistemic and aleatoric uncertainties, and incorporating uncertainty into planning and model learning processes. These approaches collectively enhance the reliability, safety, and performance of RL models in various applications.
Sources and full results
Most relevant research papers on this topic
3
Estimating Risk and Uncertainty in Deep Reinforcement Learning
3
71 Citations
2019