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Explainable Reinforcement Learning Through Goal-Based Explanations
Our key contribution to improve explainability is introducing goal-based explanations, a new explanation mechanism where the agent produces goals and attempts to reach those goals one-by-one while maximizing the collected reward.
An interpretable semi-supervised classifier using two different strategies for amended self-labeling
In the context of some machine learning applications, obtaining data instances is a relatively easy process but labeling them could become quite expensive or tedious.
Learning with Options that Terminate Off-Policy
Generally, learning with longer options (like learning with multi-step returns) is known to be more efficient.
Off-Policy Reward Shaping with Ensembles
While PBRS is proven to always preserve optimal policies, its effect on learning speed is determined by the quality of its potential function, which, in turn, depends on both the underlying heuristic and the scale.
Off-Policy Shaping Ensembles in Reinforcement Learning
Recent advances of gradient temporal-difference methods allow to learn off-policy multiple value functions in parallel with- out sacrificing convergence guarantees or computational efficiency.
