On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment

• URL: http://arxiv.org/abs/2301.09420v1
• Date: Fri, 20 Jan 2023 03:17:16 GMT
• Title: On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment
• Authors: Ansh Mittal, Aditya Malte
• Abstract summary: Multi-Agent RL or MARL is one of the complex problems in Autonomous Driving literature that hampers the release of fully-autonomous vehicles today. Several simulators have been in iteration after their inception to mitigate the problem of complex scenarios with multiple agents in Autonomous Driving.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-Agent RL or MARL is one of the complex problems in Autonomous Driving literature that hampers the release of fully-autonomous vehicles today. Several simulators have been in iteration after their inception to mitigate the problem of complex scenarios with multiple agents in Autonomous Driving. One such simulator--SMARTS, discusses the importance of cooperative multi-agent learning. For this problem, we discuss two approaches--MAPPO and MADDPG, which are based on-policy and off-policy RL approaches. We compare our results with the state-of-the-art results for this challenge and discuss the potential areas of improvement while discussing the explainability of these approaches in conjunction with waypoints in the SMARTS environment.

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