  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.
