# Reinforcement Learning Methods for Emulating Personality in a Game Environment +
## 3 Materials and Methods
### Game Mechanics and Environments
#### 3.1.4 Training Methodology
To find configurations that optimize agent performance during training, we carried out extensive experiments across a wide range of hyperparameter values, as indicated in Table 2. For example, learning rates varied from 0.003 to 0.005, buffer sizes from 64,000 to 516,000, and batch sizes from 64 to 2048. In the same way, we experimented with different hidden unit sizes (256 to 512), layers (2 to 3), and training epochs (3 to 5) in order to change the model's complexity. We were able to record the performance landscape in both low- and high-processing settings thanks to this thorough sweep. Since we needed lean and efficient training for simpler environments and more capacity and cautious updates for complex, interactive settings, we made an effort to maintain a consistent hyperparameter tuning approach. We found that significantly different optimal configurations were needed for single-agent setups and multi-agent teams with different personality traits. This disparity makes sense since multi-agent systems, especially those that simulate social dynamics like extroversion or agreeableness, inevitably entail more intricate coordination, emergent behaviors, and inter-agent interactions. More specifically, single-agent setups and multi-agent teams had very different optimal configurations as seen in Table 2. Moderate parameters like 28 for the batch size, 128,000 for the buffer size, and 0.005 for the learning rate worked best for single agents, indicating that a balance between learning stability and exploration was adequate for isolated behavior modeling. On the other hand, more aggressive and resource-intensive configurations were needed for the multi-agent system. With a slightly lower learning rate of 0.004 and a significantly larger batch size of 2048 and buffer size of 256,000, optimal performance was achieved. This modification most likely reflects the emergent coordination in socially diverse teams as well as the increased complexity and non-stationarity brought about by inter-agent interactions. Furthermore, multi-agent teams benefited from deeper architectures with three layers, 512 hidden units, and extended training over five epochs, whereas single agents performed well with two hidden layers and three training epochs. The intuition that multi-agent environments, particularly those that incorporate personality traits like agreeableness or extroversion, require more training iterations and richer representations to capture the dynamics of social coordination is supported by these findings. Since the single agents task demands were simpler and the learning space was less complicated, these setups with less complex behavior requirements converged effectively with smaller networks and smaller replay buffers. Thus, the need to efficiently capture and optimize the complex, socially influenced behavior patterns inherent to personality-drivenmulti-agent teams led to the decision to increase training parameters and allocate more computational resources. On the other hand, the complexity of the learning problem is increased when multi-agent teams are required to learn not only how to complete their individual tasks but also how to react adaptively to the actions of others in the environment. Larger neural network architectures (i.e., more hidden units) helped these teams better capture the nuances of non-linear dependencies and social behavior patterns. In order to stabilize training, larger batch sizes and replay buffer capacities were also necessary because they gave the agents access to a wider variety of experiences. This enhanced generalization across a range of social contexts and prevented overfitting to recent experiences. All single agents were trained for two million steps in each two-hour training session, and multi-agent teams were trained for ten million steps in eight hours. In order to evaluate the agents' performance and gain a better understanding of how they were interacting and learning in the environment, we looked at (group) rewards, average episode durations, and escape success rate.