Agent57: Outperforming the Atari Human Benchmark (2003.13350v1)

Abstract: Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade. This benchmark was proposed to test general competency of RL algorithms. Previous work has achieved good average performance by doing outstandingly well on many games of the set, but very poorly in several of the most challenging games. We propose Agent57, the first deep RL agent that outperforms the standard human benchmark on all 57 Atari games. To achieve this result, we train a neural network which parameterizes a family of policies ranging from very exploratory to purely exploitative. We propose an adaptive mechanism to choose which policy to prioritize throughout the training process. Additionally, we utilize a novel parameterization of the architecture that allows for more consistent and stable learning.

• The paper introduces Agent57, a deep RL agent that achieves human-level performance on all Atari games through innovative policy family parameterization.
• It utilizes an adaptive, non-stationary multi-arm bandit to balance exploration with exploitation, effectively addressing long-term credit assignment challenges.
• The architecture separates intrinsic and extrinsic value functions, significantly enhancing training stability across diverse reward structures.

Agent57: Outperforming the Atari Human Benchmark

The paper "Agent57: Outperforming the Atari Human Benchmark" presents a notable advancement in the field of reinforcement learning (RL) by introducing Agent57, a deep RL agent specifically designed to surpass the human benchmark across all 57 Atari games. This achievement marks a significant milestone in evaluating the general competency of RL algorithms within the constraints of the Arcade Learning Environment (ALE).

Overview and Contributions

The authors address the limitations of previous RL algorithms, such as Deep Q-Networks (DQN), MuZero, and R2D2, which, while achieving high performance in many games, often struggled or completely failed in others due to issues like long-term credit assignment and efficient exploration. To overcome these challenges, Agent57 incorporates several innovative strategies:

1. Policy Family Parameterization: The agent is trained using a neural network that parameterizes a range of policies from exploratory to exploitative. This spectrum allows Agent57 to dynamically adapt to the unique challenges posed by each game.
2. Adaptive Mechanism: A non-stationary multi-arm bandit algorithm is utilized to prioritize which policy to adopt during training and evaluation dynamically. This mechanism enables the agent to adjust the exploration-exploitation trade-off, optimizing resource allocation based on learning progress.
3. Improved Training Stability: The architecture is adjusted to separately parameterize the intrinsic and extrinsic components of the value function. This separation significantly enhances training stability across games with different reward structures.

Numerical Results and Performance

Agent57 achieves a capped human normalized score (CHNS) of 100%, indicating performance above the human benchmark uniformly across all Atari games. This comprehensive success contrasts with earlier efforts where agents excelled in some games but underperformed in others. For instance, MuZero achieved remarkable results in certain games with scores exceeding 1000% HNS but failed in games like Venture. Agent57’s balanced capability across all games highlights its generality and robustness.

In challenging games such as Montezuma’s Revenge and Skiing, known for their complex state spaces and sparse rewards, Agent57 successfully navigated and exceeded human-level performance without reliance on human demonstrations, marking a significant step forward in the development of autonomous agents.

Implications and Future Directions

The implications of this research extend beyond achieving human-level performance in Atari games. The methodologies introduced could be adapted to other RL domains requiring general competencies, particularly where exploration and credit assignment present significant hurdles. Furthermore, the adaptive mechanisms and network parameterizations offer insights into designing more resilient and versatile RL systems.

Future research could focus on enhancing the data efficiency of Agent57, thus reducing computational demands, a common challenge in deep RL. Additionally, exploring the application of Agent57's mechanisms in more complex and diverse environments would further test its scalability and adaptability, offering broader AI applicability.

In conclusion, Agent57 represents a substantial contribution to RL, offering a comprehensive solution to longstanding challenges in the Atari benchmark. The adaptability and stability introduced by its novel architectures and training methodologies pave the way for more advanced and capable RL systems in diverse applications.