Target localization using Multi-Agent Deep Reinforcement Learning with Proximal Policy Optimization.
Ahmed AlaghaShakti SinghRabeb MizouniJamal BentaharHadi OtrokPublished in: Future Gener. Comput. Syst. (2022)
Keyphrases
- reinforcement learning
- multi agent
- optimal policy
- policy search
- markov decision process
- reinforcement learning algorithms
- reinforcement learning problems
- function approximation
- policy iteration
- policy evaluation
- markov decision problems
- control policy
- action selection
- optimization problems
- markov decision processes
- state space
- global optimization
- multi agent environments
- temporal difference
- multi agent systems
- actor critic
- policy gradient
- approximate dynamic programming
- optimization algorithm
- optimization method
- action space
- machine learning
- multi agent reinforcement learning
- cooperative
- reinforcement learning methods
- control policies
- rl algorithms
- learning agents
- partially observable markov decision processes
- single agent
- partially observable
- multiagent systems
- exploration exploitation tradeoff
- decision problems
- partially observable environments
- state and action spaces
- agent learns
- reinforcement learning agents
- multiagent reinforcement learning
- learning problems
- transfer learning
- learning process
- function approximators
- continuous state
- localization algorithm