A Benchmark Environment For Offline Reinforcement Learning In Racing Games

Abstract

Offline Reinforcement Learning (ORL) is a promising approach to reduce the high sample complexity of traditional Reinforcement Learning (RL) by eliminating the need for continuous environmental interactions. ORL exploits a dataset of pre-collected transitions and thus expands the range of application of RL to tasks in which the excessive environment queries increase training time and decrease efficiency, such as in modern AAA games. This paper introduces OfflineMania a novel environment for ORL research. It is inspired by the iconic TrackMania series and developed using the Unity 3D game engine. The environment simulates a single-agent racing game in which the objective is to complete the track through optimal navigation. We provide a variety of datasets to assess ORL performance. These datasets, created from policies of varying ability and in different sizes, aim to offer a challenging testbed for algorithm development and evaluation. We further establish a set of baselines for a rang

Stats

• citations1
• S2 citations—
• github stars0
• HF likes0
• heat score2.26
• arxiv keymacaluso2024a

Related papers

• Neorl-2: Near Real-world Benchmarks For Offline Reinforcement Learning With Extended Realistic Scenarios (2025)0.00
• Hokoff: Real Game Dataset From Honor Of Kings And Its Offline Reinforcement Learning Benchmarks (2024)0.00
• Offline Fictitious Self-play For Competitive Games (2024)0.00
• D4RL: Datasets For Deep Data-driven Reinforcement Learning (2020)0.00
• Using Offline Data To Speed Up Reinforcement Learning In Procedurally Generated Environments (2023)6.77
• AWAC: Accelerating Online Reinforcement Learning With Offline Datasets (2020)0.00
• Towards Data-driven Offline Simulations For Online Reinforcement Learning (2022)0.00
• RL Unplugged: A Suite Of Benchmarks For Offline Reinforcement Learning (2020)0.00