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Autonomously trained agents that are supposed to play video games reasonablywell rely either on fast simulation speeds or heavy parallelization acrossthousands of machines running concurrently. This work explores a third way thatis established in robotics, namely sim-to-real transfer, or if the game isconsidered a simulation itself, sim-to-sim transfer. In the case of RocketLeague, we demonstrate that single behaviors of goalies and strikers can besuccessfully learned using Deep Reinforcement Learning in the simulationenvironment and transferred back to the original game. Although the implementedtraining simulation is to some extent inaccurate, the goalkeeping agent savesnearly 100% of its faced shots once transferred, while the striking agentscores in about 75% of cases. Therefore, the trained agent is robust enough andable to generalize to the target domain of Rocket League.

On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer