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Tom Melham

2 accepted papers

2026

Symbolic Task Inference in Deep Reinforcement Learning (Abstract Reprint)

AAAI 2026technical

This paper proposes DeepSynth, a method for effective training of deep reinforcement learning agents when the reward is sparse or non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for

Cited by 0SourcePDFScholar
2021

DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning

AAAI 2021technical

This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorith…