- The paper presents the Procgen Benchmark, a suite of 16 procedurally generated environments that assess RL algorithms' generalization and sample efficiency.
- The methodology leverages tunable, high-diversity environments optimized for fast evaluation within 200 million timesteps to challenge agents' learning capabilities.
- Experimental results show that expanding the training set to 500 levels reduces overfitting, with larger IMPALA-inspired models outperforming smaller ones.
Leveraging Procedural Generation to Benchmark Reinforcement Learning
The paper introduces the Procgen Benchmark, a suite of 16 procedurally generated environments intended to evaluate reinforcement learning (RL) algorithms with a focus on sample efficiency and generalization. These environments provide a nuanced landscape where RL agents must demonstrate robust learning capabilities across varied scenarios, moving beyond overfitting tendencies observed in traditional benchmarks such as the Arcade Learning Environment (ALE).
Key Components
Benchmark Design: The environments within the Procgen Benchmark are crafted to demand generalization, leveraging procedural content generation. This results in a near-infinite supply of diverse and randomized content, challenging agents to learn cohesive policies across varying environments without relying on memorization. Importantly, these environments are open-source and aim to bridge the gap between the diversity of ALE and the necessity for generalization, offering distinct training and test sets for comprehensive evaluation.
Environment Features:
- High Diversity: Procedural generation injects significant variability, presenting generalization challenges that require agents to learn adaptable policies.
- Fast Evaluation: Optimized for high-speed evaluation, these environments allow significant progress within 200 million timesteps, aligning with established precedents like ALE.
- Tunable Features: The difficulty in each environment can be adjusted, catering to different computational resources and enabling scalability of experiments.
Experimental Protocols
The standard experimental protocol utilizes Proximal Policy Optimization (PPO) across 200 million timesteps. For ease of comparison and efficient use of computational resources, the authors provide baseline measures for both sample efficiency and generalization, recommending a training set of 500 levels for generalization tasks.
Numerical Results and Observations
Agents trained on the Procgen Benchmark exhibit a significant degree of overfitting when trained on limited levels, showing improved performance as the training set size increases. This indicates that procedural generation plays a crucial role in facilitating effective exploration and generalization. Larger models, as demonstrated, significantly outperform smaller ones in both sample efficiency and generalization by utilizing IMPALA-inspired architectures.
Implications and Future Directions
The Procgen Benchmark stands as a pivotal tool in RL, emphasizing procedural generation as a method for creating robust and diverse training environments. The paper suggests insightful directions for future research, particularly in scaling network architectures and exploring different RL algorithms. Comparing algorithms like PPO and Rainbow within this varied and challenging suite could unlock further understanding and improvements in RL strategies.
Overall, the Procgen Benchmark offers a robust platform for advancing RL research, encouraging the development of algorithms capable of broad generalization. By tackling the intrinsic challenges of generalization, sample efficiency, exploration, and memory, it sets a new standard for evaluating and enhancing RL methodologies with potential implications for more complex, real-world applications.