  The ability to efficiently explore high-dimensional state spaces is essential
for the practical success of deep Reinforcement Learning (RL). This paper
introduces a new exploration technique called Random Latent Exploration (RLE),
that combines the strengths of bonus-based and noise-based (two popular
approaches for effective exploration in deep RL) exploration strategies. RLE
leverages the idea of perturbing rewards by adding structured random rewards to
the original task rewards in certain (random) states of the environment, to
encourage the agent to explore the environment during training. RLE is
straightforward to implement and performs well in practice. To demonstrate the
practical effectiveness of RLE, we evaluate it on the challenging Atari and
IsaacGym benchmarks and show that RLE exhibits higher overall scores across all
the tasks than other approaches.
