One-shot learning and behavioral eligibility traces in sequential decision making

Learning and Sequential Decision Making

Structure Learning in Human Sequential Decision-Making

Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. ...

Multi-shot Pedestrian Re-identification via Sequential Decision Making

Multi-shot pedestrian re-identification problem is at the core of surveillance video analysis. It matches two tracks of pedestrians from different cameras. In contrary to existing works that aggregate single frames features by time series model such as recurrent neural network, in this paper, we propose an interpretable reinforcement learning based approach to this problem. Particularly, we tra...

Evidence for eligibility traces in human learning

Whether we prepare a coffee or navigate to a shop: in many tasks we make multiple decisions before reaching a goal. Learning such state-action sequences from sparse reward raises the problem of creditassignment: which actions out of a long sequence should be reinforced? One solution provided by reinforcement learning (RL) theory is the eligibility trace (ET); a decaying memory of the state-acti...

Convergence in a sequential two stages decision making process

We analyze a sequential decision making process, in which at each stepthe decision is made in two stages. In the rst stage a partially optimalaction is chosen, which allows the decision maker to learn how to improveit under the new environment. We show how inertia (cost of changing)may lead the process to converge to a routine where no further changesare made. We illustrate our scheme with some...