Random Latent Exploration for Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2407.13755v1
• Date: Thu, 18 Jul 2024 17:55:22 GMT
• Title: Random Latent Exploration for Deep Reinforcement Learning
• Authors: Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal,
• Abstract summary: This paper introduces a new exploration technique called Random Latent Exploration (RLE) RLE combines the strengths of bonus-based and noise-based (two popular approaches for effective exploration in deep RL) exploration strategies. 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.
• Score: 71.88709402926415
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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