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Found 8 papers, 0 papers with code
CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning
We evaluate CoverNav using the Unity simulation environment and show that it guarantees dynamically feasible velocities in the terrain when fed with an elevation map generated by another DRL based navigation algorithm.
Emergent Behaviors in Multi-Agent Target Acquisition
Only limited studies and superficial evaluations are available on agents' behaviors and roles within a Multi-Agent System (MAS).
Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II
Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-quality data covering the most likely scenarios to be encountered when the agent is operating.
Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination
Reinforcement learning (RL) approaches can illuminate emergent behaviors that facilitate coordination across teams of agents as part of a multi-agent system (MAS), which can provide windows of opportunity in various military tasks.
On games and simulators as a platform for development of artificial intelligence for command and control
Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces.
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training
Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
On Memory Mechanism in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Coordination-driven learning in multi-agent problem spaces
We discuss the role of coordination as a direct learning objective in multi-agent reinforcement learning (MARL) domains.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
