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Found 7 papers, 2 papers with code
A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertainty
In this paper, we propose an algorithm that clarifies the theoretical connection between aleatory and epistemic uncertainty, unifies aleatory and epistemic uncertainty estimation, and quantifies the combined effect of both uncertainties for a risk-sensitive exploration.
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learning
Distributional Reinforcement Learning (RL) estimates return distribution mainly by learning quantile values via minimizing the quantile Huber loss function, entailing a threshold parameter often selected heuristically or via hyperparameter search, which may not generalize well and can be suboptimal.
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning
Putting together two ideas of hybrid model-based successor feature (MB-SF) and uncertainty leads to an approach to the problem of sample efficient uncertainty-aware knowledge transfer across tasks with different transition dynamics or/and reward functions.
Active Inference and Reinforcement Learning: A unified inference on continuous state and action spaces under partially observability
Despite this exploratory behaviour of AIF, its usage is limited to discrete spaces due to the computational challenges associated with EFE.
AKF-SR: Adaptive Kalman Filtering-based Successor Representation
Recent studies in neuroscience suggest that Successor Representation (SR)-based models provide adaptation to changes in the goal locations or reward function faster than model-free algorithms, together with lower computational cost compared to that of model-based algorithms.
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation
The proposed MAK-TD/SR frameworks consider the continuous nature of the action-space that is associated with high dimensional multi-agent environments and exploit Kalman Temporal Difference (KTD) to address the parameter uncertainty.
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning
As a result, the proposed MM-KTD framework can learn the optimal policy with significantly reduced number of samples as compared to its DNN-based counterparts.
