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Found 6 papers, 4 papers with code
Using deep Q-learning to understand the tax evasion behavior of risk-averse firms
By doing so, we i) determine the tax evasion behavior expected of the taxpayer entity, ii) calculate the degree of risk aversion of the "average" entity given empirical estimates of tax evasion, and iii) evaluate sample tax policies, in terms of expected revenues.
Cortical microcircuits as gated-recurrent neural networks
Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas.
LipNet: End-to-End Sentence-level Lipreading
Lipreading is the task of decoding text from the movement of a speaker's mouth.
Ranked #5 on
Lipreading
on GRID corpus (mixed-speech)
Multi-Objective Deep Reinforcement Learning
We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-objective decision problems where the relative importances of the objectives are not known a priori.
Multi-Objective Reinforcement Learning
reinforcement-learning
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility.
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
We propose deep distributed recurrent Q-networks (DDRQN), which enable teams of agents to learn to solve communication-based coordination tasks.
