LLM-generated, iteratively refined reward functions guide multi-agent PPO to 100% success in formation control with collision avoidance in a three-agent benchmark.
Monotonic value function factorisation for deep multi- agent reinforcement learning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Application of LLM Guided Reinforcement Learning in Formation Control with Collision Avoidance
LLM-generated, iteratively refined reward functions guide multi-agent PPO to 100% success in formation control with collision avoidance in a three-agent benchmark.