During the training of a DDPG (Deep Deterministic Policy Gradient) model in a reinforcement learning toolbox, there are several quality attributes or metrics that you can consider to evaluate and monitor the performance of the model. Here are some commonly used ones:
1. **Reward**: The reward metric measures the cumulative rewards obtained by the agent during training. It reflects the effectiveness of the learned policy in achieving the desired task or objective. Monitoring the reward over time can provide insights into the learning progress and the effectiveness of the agent's actions.
2. **Episode Length**: Episode length refers to the number of steps or time-steps taken by the agent to complete an episode. It can be an important metric to track, as it can indicate how quickly the agent is able to solve the task or how long it takes to converge to a solution.
3. **Exploration vs. Exploitation**: In reinforcement learning, striking a balance between exploration (trying new actions to learn more about the environment) and exploitation (using the learned policy to maximize rewards) is crucial. Monitoring the exploration vs. exploitation trade-off can help ensure that the agent is adequately exploring the state-action space while also exploiting the learned policy.
4. **Policy Loss**: The policy loss metric measures the discrepancy between the predicted actions of the policy network and the actions taken by the agent. Minimizing the policy loss is essential for training an effective policy network.
5. **Value Loss**: The value loss metric quantifies the discrepancy between the predicted state values of the value network and the actual observed rewards. Minimizing the value loss helps in training an accurate value network that can estimate the expected return accurately.
6. **Actor-Critic Loss**: DDPG consists of an actor network and a critic network. The actor network learns the policy, while the critic network learns the value function. Monitoring the actor-critic loss can provide insights into the convergence and stability of both networks.
7. **Exploration Noise**: DDPG often utilizes exploration noise to encourage exploration during training. Monitoring the exploration noise and its decay over time can help understand how the agent's exploration strategy evolves during training.
8. **Convergence**: Convergence refers to the point at which the agent's performance stabilizes or reaches a satisfactory level. Monitoring the convergence of the model can help determine when to stop training or whether further improvements are possible.
These metrics can be tracked during training to evaluate the performance and progress of the DDPG model. They can also be used to fine-tune hyperparameters and optimize the training process. However, the specific choice of metrics may vary depending on the nature of the task and the specific requirements of the application.
