Benchmarking safe exploration in deep reinforcement learning
Reinforcement learning (RL) agents need to explore their environments in order to learn optimal policies by trial and error. In many environments, safety is a critical concern and certain errors are unacceptable: for example, robotics systems that interact...
Topics: AgentsInfrastructure
Entities: AgentsInfrastructure