It is an abstract class that implements an agent for reinforcement learning. This class can be extended to create specific agents for different environments.

The constructor accepts the following parameters:

• num_actions: The number of possible actions that the agent can take.
• environment: The environment in which the agent operates, represented as a numpy array.
• fit_each_n_steps: The number of steps after which the agent trains its models.
• exploration_rate: The probability that the agent chooses a random action instead of the optimal action.
• exploration_rate_decay: The decay rate of the exploration rate.
• gamma: The discount factor used in the Q-value update equation.
• cumulative_rewards_max_length: The maximum length of the array that keeps track of cumulative rewards.
• memory_max_length: The maximum length of the agent's memory.
• memory_batch_size: The batch size used for training the models.
• allow_episode_tracking: A boolean flag indicating whether the agent should track episodes.

The BaseAgent class implements the following methods:

• start_episode: Starts a new episode.
• stop_episode: Ends the current episode.
• get_episodes: Returns all recorded episodes.
• reset_episodes: Resets all recorded episodes.
• is_memory_ready: Checks whether the agent's memory is ready for training.
• step: Performs a step of the agent, choosing an action, receiving a reward, and updating the models.
• get_last_cumulative_rewards: Returns the sum of the last cumulative rewards.

In addition, the it takes the following functions to be implemented in subclasses:

• reset_state: Resets the agent's state at the start of a new episode.
• _get_reward: Calculates the reward received by the agent for undertaking an action in a given state.
• _get_model: Returns the model used by the agent to learn the Q-value function.

To use the class, you need to extend it and implement the abstract methods. Here's an example of how this might be done:

class MyAgent(Agent):
    def reset_state(self):
        # Implementation of state reset

    def _get_reward(self, action, environment):
        # Implementation of reward calculation

    def _get_model(self, state_features):
        # Implementation of the model

Once the subclass is defined, you can create an instance of the agent and use it as follows:

agent = MyAgent(num_actions=4, environment=np.array([0, 0, 0, 0]))
agent.start_episode()
for _ in range(100):
    agent.step()
agent.stop_episode()

BaseAgent is designed to be used with discrete environments and deep learning models. If you wish to use a continuous environment or a different learning model, you may need to make some modifications to the class.