Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPO

15 Jan 2020 Mario S. Holubar Marco A. Wiering

In this paper, a novel racing environment for OpenAI Gym is introduced. This environment operates with continuous action- and state-spaces and requires agents to learn to control the acceleration and steering of a car while navigating a randomly generated racetrack... (read more)

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Methods used in the Paper


METHOD TYPE
Entropy Regularization
Regularization
PPO
Policy Gradient Methods
Experience Replay
Replay Memory