Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld (2405.16946v1)

Abstract: How do biological systems and machine learning algorithms compare in the number of samples required to show significant improvements in completing a task? We compared the learning efficiency of in vitro biological neural networks to the state-of-the-art deep reinforcement learning (RL) algorithms in a simplified simulation of the game `Pong'. Using DishBrain, a system that embodies in vitro neural networks with in silico computation using a high-density multi-electrode array, we contrasted the learning rate and the performance of these biological systems against time-matched learning from three state-of-the-art deep RL algorithms (i.e., DQN, A2C, and PPO) in the same game environment. This allowed a meaningful comparison between biological neural systems and deep RL. We find that when samples are limited to a real-world time course, even these very simple biological cultures outperformed deep RL algorithms across various game performance characteristics, implying a higher sample efficiency. Ultimately, even when tested across multiple types of information input to assess the impact of higher dimensional data input, biological neurons showcased faster learning than all deep reinforcement learning agents.

• The paper finds that biological neuron cultures outperform state-of-the-art deep reinforcement learning algorithms like DQN, A2C, and PPO in sample efficiency within a simulated gameworld.
• Utilizing the DishBrain system, researchers showed that biological neurons, including human and mouse cortical cells, learned faster and required fewer environmental interactions than tested RL models.
• The study suggests biological systems' intrinsic plasticity and adaptive traits offer insights for developing more sample-efficient and adaptable artificial learning algorithms, potentially bridging neuroscience and AI.

An Examination of Biological Neurons and Deep Reinforcement Learning in Sample Efficiency

The paper titled "Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld" explores a comparative analysis between in vitro biological neural networks and state-of-the-art deep reinforcement learning (RL) algorithms. Utilizing a simplified simulation of the classic game 'Pong', the researchers employed DishBrain, an advanced system integrating biological neurons with in silico computation through high-density multi-electrode arrays. This comparison focused on the learning rate and sample efficiency of biological systems versus three popular RL algorithms: DQN, A2C, and PPO.

Key Findings

In environments with constrained samples, the paper reveals that biological neural cultures outperform conventional deep RL algorithms across various gameplay metrics. The biological systems not only demonstrated faster learning but also greater sample efficiency, requiring fewer interactions with the environment to achieve commendable performance. Notably, both human cortical cells (HCCs) and mouse cortical cells (MCCs) exhibited superior capabilities in terms of the average hits-per-rally and managed to minimize initial faults like aces more effectively than their RL counterparts.

Methodological Insights

The research introduces several variations of input designs for the RL algorithms to accurately simulate the conditions faced by biological neurons. These include the traditional Image Input, and control conditions mimicking the biological setup such as Paddle and Ball Position Inputs. It was observed that despite reducing the dimensionality of input data, which theoretically should improve the RL algorithm's sample efficiency, the biological systems still maintained an edge in performance. This superior learning trait is attributed to the intrinsic plasticity and adaptive characteristics of neuronal systems.

Moreover, the paper also highlights the disproportionate computational power demands of RL algorithms compared to biological systems, thus emphasizing the efficiency of the latter not only in terms of learning speed but also energy usage.

Implications and Future Directions

The results underscore the potential of biological neural networks as viable learning machines, offering insights that could be translated into more efficient artificial learning systems. While current RL models are adept at achieving high performance in static environments over extended training periods, this paper accentuates the necessity for developing models with improved sample efficiency and adaptability to dynamic contexts.

Biological systems inherently possess mechanisms for rapid adaptability and learning, as evidenced by their neuroplasticity. Future research may explore bio-inspired algorithms that mirror these traits, potentially revolutionizing the AI field. Techniques such as synaptic plasticity, predictive coding, and active inference could offer frameworks for the next generation of RL algorithms, bridging the gap between biological learning and artificial intelligence.

In conclusion, while deep RL algorithms have shown prowess in controlled settings, the adaptability and efficiency inherent in biological neurons present an exciting avenue for further exploration. The intersection of neuroscience and machine learning, as highlighted by this paper, holds significant promise for advancing both theoretical understanding and practical implementations in AI.