  Current reinforcement learning algorithms train an agent using
forward-generated trajectories, which provide little guidance so that the agent
can explore as much as possible. While realizing the value of reinforcement
learning results from sufficient exploration, this approach leads to a
trade-off in losing sample efficiency, an essential factor impacting algorithm
performance. Previous tasks use reward-shaping techniques and network structure
modification to increase sample efficiency. However, these methods require many
steps to implement. In this work, we propose novel backward curriculum
reinforcement learning that begins training the agent using the backward
trajectory of the episode instead of the original forward trajectory. This
approach provides the agent with a strong reward signal, enabling more
sample-efficient learning. Moreover, our method only requires a minor change in
the algorithm of reversing the order of the trajectory before agent training,
allowing a straightforward application to any state-of-the-art algorithm.
