Search Results for author:
Found 11 papers, 1 papers with code
Altitude-Loss Optimal Glides in Engine Failure Emergencies -- Accounting for Ground Obstacles and Wind
Engine failure is a recurring emergency in General Aviation and fixed-wing UAVs, often requiring the pilot or remote operator to carry out carefully planned glides to safely reach a candidate landing strip.
Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance
We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated.
Deep Randomized Least Squares Value Iteration
Rather than using hand-design state representation, we use a state representation that is being learned directly from the data by a DQN agent.
ILS-SUMM: Iterated Local Search for Unsupervised Video Summarization
We consider shot-based video summarization where the summary consists of a subset of the video shots which can be of various lengths.
Learning Control for Air Hockey Striking using Deep Reinforcement Learning
We consider the task of learning control policies for a robotic mechanism striking a puck in an air hockey game.
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance.
The Max $K$-Armed Bandit: PAC Lower Bounds and Efficient Algorithms
Under the PAC framework, we provide a lower bound on the sample complexity of any $(\epsilon,\delta)$-correct algorithm, and propose an algorithm that attains this bound up to logarithmic factors.
The Max $K$-Armed Bandit: A PAC Lower Bound and tighter Algorithms
We consider the Max $K$-Armed Bandit problem, where a learning agent is faced with several sources (arms) of items (rewards), and interested in finding the best item overall.
An Online Convex Optimization Approach to Blackwell's Approachability
The notion of approachability in repeated games with vector payoffs was introduced by Blackwell in the 1950s, along with geometric conditions for approachability and corresponding strategies that rely on computing {\em steering directions} as projections from the current average payoff vector to the (convex) target set.
Response-Based Approachability and its Application to Generalized No-Regret Algorithms
The first (primary) condition is a geometric separation condition, while the second (dual) condition requires that the set be {\em non-excludable}, namely that for every mixed action of the opponent there exists a mixed action of the agent (a {\em response}) such that the resulting payoff vector belongs to $S$.
Online Classification with Specificity Constraints
To our best knowledge, this is the first algorithm that addresses the problem of the average tp-rate maximization under average fp-rate constraints in the online setting.
