Search Results for author: Nicholas Bishop

Found 4 papers, 0 papers with code

Interventionally Consistent Surrogates for Agent-based Simulators

no code implementations18 Dec 2023 Joel Dyer, Nicholas Bishop, Yorgos Felekis, Fabio Massimo Zennaro, Anisoara Calinescu, Theodoros Damoulas, Michael Wooldridge

Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents.

Explicit Explore, Exploit, or Escape ($E^4$): near-optimal safety-constrained reinforcement learning in polynomial time

no code implementations14 Nov 2021 David M. Bossens, Nicholas Bishop

Constrained Markov decision processes (CMDPs) can provide long-term safety constraints; however, the agent may violate the constraints in an effort to explore its environment.

Reinforcement Learning (RL)

Adversarial Blocking Bandits

no code implementations NeurIPS 2020 Nicholas Bishop, Hau Chan, Debmalya Mandal, Long Tran-Thanh

On the other hand, when B_T is not known, we show that the dynamic approximate regret of RGA-META is at most O((K+\tilde{D})^{1/4}\tilde{B}^{1/2}T^{3/4}) where \tilde{B} is the maximal path variation budget within each batch of RGA-META (which is provably in order of o(\sqrt{T}).

Blocking

Optimal Learning from Verified Training Data

no code implementations NeurIPS 2020 Nicholas Bishop, Long Tran-Thanh, Enrico Gerding

In attempts to relax this assumption, fields such as adversarial learning typically assume that data is provided by an adversary, whose sole objective is to fool a learning algorithm.

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