8 accepted papers
Agents in the real world must often balance multiple objectives, such as speed, stability, and energy efficiency in continuous control. To account for changing conditions and preferences, an agent must ideally learn a Pareto frontier of policies representing multiple optimal trade-offs. Recent advan
Many critical robot environments, such as healthcare and security, require robots to account for contextdependent criteria when performing their functions (e.g., navigation). Such domains require decisions that balance multiple factors, making it difficult for robots to make contextually appropriate…
Many cooperative multiagent reinforcement learning environments provide agents with a sparse team-based reward, as well as a dense agent-specific reward that incentivizes learning basic skills. Training policies solely on the team-based reward is often difficult due to its sparsity. Also, relying so…
Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically struggle with achieving effective exploration and are extremely sensitive to the choice of hyperparameters. One reason is that most approaches use a noisy v…
Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically suffer from three core difficulties: temporal credit assignment with sparse rewards, lack of effective exploration, and brittle convergence properties…
Autonomous multi-robot teams can be used in complex coordinated exploration tasks to improve exploration performance in terms of both speed and effectiveness. However, use of multi-robot systems presents additional challenges. Specifically, in domains where the robots' actions are tightly coupled, c…
Multi-robot teams offer key advantages over single robots in exploration missions by increasing efficiency (explore larger areas), reducing risk (partial mission failure with robot failures), and enabling new data collection modes (multi-modal observations). However, coordinating multiple robots to…
In this paper we consider the problem of routing autonomously guided robots by manipulating the cost space to induce safe trajectories in the work space. Specifically, we examine the domain of UAV traffic management in urban airspaces. Each robot does not explicitly coordinate with other vehicles in…