Increase the training iterations: Train the PPO model for more iterations, as the model might not have converged yet.
Adjust the PPO hyperparameters: Experiment with different hyperparameters such as learning rate, batch size, and discount factor. refer to the Stable Baselines documentation for more details.
Modify the MLP architecture: Adjust the number of layers and neurons in the MLP, as well as the activation functions, to improve the model's capacity to learn complex patterns.
Experiment with other algorithms: There are several other reinforcement learning algorithms available in the Stable Baselines library, such as DDPG, SAC, and A2C. experiment with these.
Increase the training iterations: Train the PPO model for more iterations, as the model might not have converged yet.
Adjust the PPO hyperparameters: Experiment with different hyperparameters such as learning rate, batch size, and discount factor. refer to the Stable Baselines documentation for more details.
Modify the MLP architecture: Adjust the number of layers and neurons in the MLP, as well as the activation functions, to improve the model's capacity to learn complex patterns.
Experiment with other algorithms: There are several other reinforcement learning algorithms available in the Stable Baselines library, such as DDPG, SAC, and A2C. experiment with these.