Shaped Policy Search for Evolutionary Strategies using Waypoints

• URL: http://arxiv.org/abs/2105.14639v2
• Date: Mon, 3 Jul 2023 06:09:53 GMT
• Title: Shaped Policy Search for Evolutionary Strategies using Waypoints
• Authors: Kiran Lekkala, Laurent Itti
• Abstract summary: We try to improve exploration in Blackbox methods, particularly Evolution strategies (ES) We use the state-action pairs from the trajectories obtained during rollouts/evaluations to learn the dynamics of the agent. The learnt dynamics are then used in the optimization procedure to speed-up training.
• Score: 17.8055398673228
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we try to improve exploration in Blackbox methods, particularly Evolution strategies (ES), when applied to Reinforcement Learning (RL) problems where intermediate waypoints/subgoals are available. Since Evolutionary strategies are highly parallelizable, instead of extracting just a scalar cumulative reward, we use the state-action pairs from the trajectories obtained during rollouts/evaluations, to learn the dynamics of the agent. The learnt dynamics are then used in the optimization procedure to speed-up training. Lastly, we show how our proposed approach is universally applicable by presenting results from experiments conducted on Carla driving and UR5 robotic arm simulators.

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