A deep reinforcement learning approach for autonomous car racing
F Guo, Z Wu - E-Learning and Games: 12th International Conference …, 2019 - Springer
F Guo, Z Wu
E-Learning and Games: 12th International Conference, Edutainment 2018, Xi'an …, 2019•SpringerIn this paper, we introduce a deep reinforcement learning approach for autonomous car
racing based on the Deep Deterministic Policy Gradient (DDPG). We start by implementing
the approach of DDPG, and then experimenting with various possible alterations to improve
performance. In particular, we exploit two strategies: the action punishment and multiple
exploration, to optimize actions in the car racing environment. We evaluate the performance
of our approach on the Car Racing dataset, the experimental results demonstrate the …
racing based on the Deep Deterministic Policy Gradient (DDPG). We start by implementing
the approach of DDPG, and then experimenting with various possible alterations to improve
performance. In particular, we exploit two strategies: the action punishment and multiple
exploration, to optimize actions in the car racing environment. We evaluate the performance
of our approach on the Car Racing dataset, the experimental results demonstrate the …
Abstract
In this paper, we introduce a deep reinforcement learning approach for autonomous car racing based on the Deep Deterministic Policy Gradient (DDPG). We start by implementing the approach of DDPG, and then experimenting with various possible alterations to improve performance. In particular, we exploit two strategies: the action punishment and multiple exploration, to optimize actions in the car racing environment. We evaluate the performance of our approach on the Car Racing dataset, the experimental results demonstrate the effectiveness of the proposed approach.
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