Meta-Reinforcement Learning of Structured Exploration Strategies
Prior tasks can be used to inform how exploration in new tasks should be performed.
A meta-RL algorithm that adapts to new tasks by following the policy gradient, while also injecting learned structured stochasticity into a latent space to enable effective exploration.
Effective exploration strategies must select randomly from among the potentially useful behaviors, while avoiding behaviors that are highly unlikely to succeed. MAESN leverages this insight to acquire significantly better exploration strategies by incorporating learned time-correlated noise through its meta-learned latent space, and training both the policy parameters and the latent exploration space explicitly for fast adaptation.