ADAPTER-RL: Adaptation of Any Agent using Reinforcement Learning.
Yizhao JinGreg G. SlabaughSimon M. LucasPublished in: CoRR (2023)
Keyphrases
- reinforcement learning
- multi agent
- action selection
- learning agent
- learning capabilities
- autonomous learning
- exploration strategy
- partially observable
- reward function
- state action
- learning agents
- agent learns
- function approximation
- multi agent environments
- action space
- multi agent systems
- state abstraction
- multiagent systems
- single agent
- reinforcement learning agents
- model free
- reinforcement learning algorithms
- state space
- markov decision processes
- markov decision process
- reward shaping
- autonomous agents
- temporal difference
- rl algorithms
- multiple agents
- intelligent agents
- optimal policy
- temporal difference learning
- machine learning
- robocup soccer
- learning algorithm
- partially observable domains
- learning process
- multiagent reinforcement learning
- learning problems
- software agents
- transfer learning
- reward signal
- dynamic environments
- multi agent reinforcement learning
- optimal control
- reinforcement learning methods
- learning classifier systems
- learned knowledge
- function approximators
- mobile agents
- continuous state
- dynamic programming
- state and action spaces
- agent model