1 citations · 2 across the 4 of their papers we have counts for
5 papers
Abstractions of General Reinforcement Learning
Sultan J. Majeed
The field of artificial intelligence (AI) is devoted to the creation of artificial decision-makers that can perform (at least) on par with the human counterparts on a domain of int…
Reducing Planning Complexity of General Reinforcement Learning with Non-Markovian Abstractions
Sultan J. Majeed, Marcus Hutter
The field of General Reinforcement Learning (GRL) formulates the problem of sequential decision-making from ground up. The history of interaction constitutes a "ground" state of th…
Exact Reduction of Huge Action Spaces in General Reinforcement Learning
Sultan Javed Majeed, Marcus Hutter
The reinforcement learning (RL) framework formalizes the notion of learning with interactions. Many real-world problems have large state-spaces and/or action-spaces such as in Go,…
Conditions on Features for Temporal Difference-Like Methods to Converge
Marcus Hutter, Samuel Yang-Zhao, Sultan J. Majeed
The convergence of many reinforcement learning (RL) algorithms with linear function approximation has been investigated extensively but most proofs assume that these methods conver…
Performance Guarantees for Homomorphisms Beyond Markov Decision Processes
Sultan Javed Majeed, Marcus Hutter
Most real-world problems have huge state and/or action spaces. Therefore, a naive application of existing tabular solution methods is not tractable on such problems. Nonetheless, t…