  Sparse reward is one of the biggest challenges in reinforcement learning
(RL). In this paper, we propose a novel method called Generative Exploration
and Exploitation (GENE) to overcome sparse reward. GENE automatically generates
start states to encourage the agent to explore the environment and to exploit
received reward signals. GENE can adaptively tradeoff between exploration and
exploitation according to the varying distributions of states experienced by
the agent as the learning progresses. GENE relies on no prior knowledge about
the environment and can be combined with any RL algorithm, no matter on-policy
or off-policy, single-agent or multi-agent. Empirically, we demonstrate that
GENE significantly outperforms existing methods in three tasks with only binary
rewards, including Maze, Maze Ant, and Cooperative Navigation. Ablation studies
verify the emergence of progressive exploration and automatic reversing.

Machine Learning;Artificial Intelligence;Machine Learning