Deep reinforcement learning-based air-to-air combat maneuver generation in a realistic environment
Artificial intelligence is becoming increasingly important in the air combat domain. Most air
combat research now assumes that all aircraft information is known. In practical applications …
combat research now assumes that all aircraft information is known. In practical applications …
Informed POMDP: Leveraging additional information in model-based RL
In this work, we generalize the problem of learning through interaction in a POMDP by
accounting for eventual additional information available at training time. First, we introduce …
accounting for eventual additional information available at training time. First, we introduce …
CEMDQN: Cognitive-inspired episodic memory in deep Q-Networks
Reinforcement learning in the field of artificial intelligence has seen tremendous advances
in recent years, but there are still several limitations standing in the way of its wider practical …
in recent years, but there are still several limitations standing in the way of its wider practical …
Observation-Time-Action Deep Stacking Strategy: Solving Partial Observability Problems with Visual Input
Reinforcement learning tasks that involve visual input continue to pose a challenge when it
comes to partial observability problems. Although prior research has introduced methods …
comes to partial observability problems. Although prior research has introduced methods …