On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment
This work addresses multi-agent reinforcement learning for autonomous driving, which is an incremental contribution focused on improving simulation-based methods.
The paper tackled the problem of cooperative multi-agent learning in autonomous driving using the SMARTS simulator, comparing MAPPO and MADDPG approaches and discussing their explainability with waypoints, but did not report specific numerical results.
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.