On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment
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Abstract (arXiv)
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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References · 12
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2015Trust Region Policy Optimizationcited by 2,503
- 2018World Modelscited by 1,444
- 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
- 2018Model-Ensemble Trust-Region Policy Optimizationcited by 214
- 2020SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Drivingcited by 121
- 2020Explainability in Deep Reinforcement Learningcited by 96
- 2020R-MADDPG for Partially Observable Environments and Limited Communicationcited by 36
- 2018An Adaptive Clipping Approach for Proximal Policy Optimizationcited by 14
- 2022Explainability in reinforcement learning: perspective and positioncited by 11
- 2019Health-Informed Policy Gradients for Multi-Agent Reinforcement Learningcited by 2
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