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.

介绍了一种名为随机潜在探索（RLE）的新的探索技术，其结合了基于奖励和基于噪声的探索策略的优点，以有效地探索深度强化学习中的高维状态空间。RLE通过在环境的特定（随机）状态中将结构化的随机奖励添加到原始任务奖励中，鼓励代理在训练过程中探索环境。RLE易于实现且在实践中表现良好。通过在具有挑战性的Atari和IsaacGym基准测试上评估RLE，发现RLE在所有任务中表现出比其他方法更高的综合得分。

随机潜在探索的深度强化学习