Search Results for author:
Found 5 papers, 0 papers with code
Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning
Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic Management (UTM).
Continuous Control for Searching and Planning with a Learned Model
Decision-making agents with planning capabilities have achieved huge success in the challenging domain like Chess, Shogi, and Go.
A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance
A novel deep multi-agent reinforcement learning framework is proposed to identify and resolve conflicts among a variable number of aircraft in a high-density, stochastic, and dynamic sector.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Prioritized Sequence Experience Replay
Experience replay is widely used in deep reinforcement learning algorithms and allows agents to remember and learn from experiences from the past.
Fast Online Exact Solutions for Deterministic MDPs with Sparse Rewards
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision making under uncertainty.
