Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research

Understand

Since the introduction of DQN, a vast majority of reinforcement learning research has focused on reinforcement learning with deep neural networks as function approximators.

• New methods are typically evaluated on a set of environments that have now become standard, such as Atari 2600 games.
• While these benchmarks help standardize evaluation, their computational cost has the unfortunate side effect of widening the gap between those with ample access to computational resources, and those without.
• In this work we argue that, despite the community's emphasis on large-scale environments, the traditional small-scale environments can still yield valuable scientific insights and can help reduce the barriers to entry for underprivileged communities.