Deep Q Learning Algorithm Explained Simply

Deep Q Learning is a type of reinforcement learning where an agent learns to make decisions by taking actions in an environment and receiving rewards or penalties.

The algorithm uses a neural network to approximate the action-value function, also known as the Q-function, which estimates the expected return or reward for a given state-action pair.

The Q-function is learned through trial and error, with the agent interacting with the environment and updating its Q-function based on the rewards it receives.

This process is repeated many times, with the agent refining its Q-function and improving its decision-making over time.

If this caught your attention, see: Action Model Learning

The Q-Table is a fundamental component of the Q-Learning algorithm, used to store the expected return or utility of each state-action pair.

It's essentially a table that maps states to actions and their corresponding Q-values.

The Q-Table is initialized with arbitrary values, often set to 0, and updated as the agent interacts with the environment.

Take a look at this: Q Learning Algorithm

This table is updated based on the Q-Update rule, which takes into account the current Q-value, the reward received, and the discount factor.

In simple problems, the Q-Table can be sufficient for achieving optimal solutions, but it quickly becomes impractical for more complex problems.

This is because the number of state-action pairs grows exponentially with the number of states and actions.

For example, in a grid world with 10x10 states and 4 possible actions, the Q-Table would have 400 possible entries.

Deep Q-Networks (DQNs) are a type of neural network that excel at modeling complex functions. They're essentially used to estimate the Q function, which maps a state to the Q values of all possible actions.

The underlying principle of a Deep Q Network is similar to the Q Learning algorithm, starting with arbitrary Q-value estimates and exploring the environment using the ε-greedy policy. It uses dual actions, a current action with a current Q-value and a target action with a target Q-value, for its update logic to improve its Q-value estimates.

A Deep Q Network typically consists of two neural networks: a Q Network and a Target Network. The Q Network is the main network that gets updated at each time step, while the Target Network remains unchanged and is used to provide stable target Q values.

Neural networks are excellent at modeling complex functions.

This makes them a perfect fit for estimating the Q function, which maps a state to the Q values of all the actions that can be taken from that state.

The underlying principle of a Deep Q Network is very similar to the Q Learning algorithm.

It starts with arbitrary Q-value estimates and explores the environment using the ε-greedy policy.

At its core, it uses the same notion of dual actions, a current action with a current Q-value and a target action with a target Q-value, for its update logic to improve its Q-value estimates.

The Target network predicts Q values for all actions that can be taken from the next state, and selects the maximum of those Q values.

We discussed earlier that the network would accept states from the environment as input. Thinking of Frozen Lake, we could easily represent the states using a simple coordinate system from the grid of the environment and use this as input.

If we're in a more complex environment, though, like a video game, for example, then we'll use images as our input. Specifically, we'll use still frames that capture states from the environment as the input to the network.

We usually will use a stack of a few consecutive frames to represent a single input. So, we would grab, say, four consecutive frames from the video game.

A single frame usually isn't going to be enough for our network, or even for our human brains, to fully understand the state of the environment. For example, by just looking at the first single frame above from the Atari game, Breakout, we can't tell if the ball is coming down to the paddle or going up to hit the block.

If we look at four consecutive frames, though, then we have a much better idea about the current state of the environment because we now do indeed have information about all of these things that we didn't know with just a single frame.

Experience Replay is a technique used in Deep Q-Learning to make more efficient use of experiences during training. It helps by using a replay buffer that saves experience samples that can be reused during the training.

The replay buffer stores experience tuples, which include the state of the agent, the action taken, the reward received, and the next state of the agent. This allows the agent to learn from the same experiences multiple times, reducing the correlation between experiences and avoiding catastrophic forgetting.

Here's a breakdown of the steps involved in Experience Replay:

By using Experience Replay, we can improve the stability and efficiency of the training process, allowing the agent to learn from a diverse range of experiences and generalize well to new situations.

Experience replay helps make more efficient use of experiences during training. It allows the agent to learn from the same experiences multiple times.

To avoid catastrophic forgetting, experience replay stores experience tuples while interacting with the environment and then samples a small batch of tuples. This prevents the network from only learning about what it has done immediately before.

A replay buffer is used to store experience samples, and a batch of samples is randomly selected from this memory. This ensures that the batch is 'shuffled' and contains enough diversity from older and newer samples.

The capacity of the replay buffer is a hyperparameter that can be defined. The more capacity the buffer has, the more experiences it can store and the more diverse the batch of samples will be.

Here are some key benefits of optimizing replay:

By optimizing replay, you can improve the performance and efficiency of your reinforcement learning model.

Fixed to Stabilize Training

Chasing a moving target is not a great strategy, especially when it comes to training a deep Q-network. This is exactly what happens when we use the same parameters for estimating the Q-value and the TD target.

The TD target is just the reward of taking that action at that state plus the discounted highest Q value for the next state, but it's constantly shifting because we're using the same parameters for both.

This can lead to significant oscillation in training, making it harder to get the desired results. It's like trying to catch a cow that's also moving at every time step.

To stabilize training, we can use a separate network with fixed parameters for estimating the TD target. This way, the target remains fixed, and we can focus on getting our Q-values closer to it.

We can update the target network by copying the parameters from our deep Q-network every C steps.

Experience Replay is a key component of the deep Q learning algorithm. It gathers training data by saving all prior observations as samples.

A random batch of samples is taken from the training data to create a mix of older and more recent samples. This helps the Q network learn from a diverse set of experiences.

The sample data, including the current state, action, reward, and next state, is saved and used to compute the loss to train the Q network. The Predicted Q Value, Target Q Value, and observed reward from the data sample are used to compute the Loss. The Target Network is not trained in this step.

Gather Training Data is a crucial step in the training process. It involves saving observations as sample data.

All prior observations are saved as training data, including those from Experience Replay. This ensures a diverse dataset for training.

Experience Replay observations are saved as training data, which is then used to create a mix of older and more recent samples. This helps prevent the model from overfitting to recent data.

We now take a random batch of samples from the training data. This batch should contain a mix of older and more recent samples to keep the model well-rounded.

The sample data, including the current state, action, reward, and next state, is saved as part of the training process. This data will later be used to train the DQN.

Experience Replay executes the ε-greedy action and receives the next state and reward. It stores the results in the replay data, where they will be used as sample observations for training.

Check this out: Difference between Model and Algorithm in Machine Learning

To train the model, we use the Predicted Q Value and the observed reward from the data sample to compute the Loss.

The Loss is computed using the difference between the Target Q Value and the Predicted Q Value, specifically the Mean Squared Error.

The Q network weights are updated using gradient descent to minimize the Loss.

The Target network remains fixed and is not trained, so no Loss is computed for it.

Back-propagation is used to update the weights of the Q Network, which helps to minimize the Loss.

The Q network and the Target network have equal weights at this stage.

Exploration and Exploitation are two fundamental concepts in the Deep Q Learning algorithm.

The agent's goal is to make the best decision given the current information, known as exploitation. However, this may result in a problem where the agent misses out on alternative actions that could lead to a better path in the long run.

The ε-Greedy policy solves this problem by allowing the AI agent to take random actions from the action-space with a certain probability ε. This is called exploration.

The value of ε decreases over time, according to equation 12, where n equals the number of iterations. Decreasing ε means that, at the beginning of training, we try to explore more alternative paths but, in the end, we let the policy make decisions on which action to take.

The ε-Greedy policy is typically chosen as the behavior policy because it allows the agent to select a random action with a fixed probability ε at each time step. If ε has a higher value than a randomly generated number p, the AI agent picks a random action from the action space.

Here's a summary of the exploration and exploitation trade-off:

Choosing ε-Greedy policy as the behavior policy µ solves the dilemma of the exploration/exploitation trade off.

Temporal Difference Learning is a key concept in deep Q-learning, and it's what enables our AI agent to learn and improve over time.

The goal of deep Q-learning is to solve the action-value function Q(s,a), which allows the agent to determine the quality of any possible action in any given state.

In TD learning, we update Q(s,a) for each action a in a state s towards the estimated return R(t+1)+γQ(s(t+1), a(t+1)).

The TD-target, which is the estimated return, is used to update the previous Q(s_t, a_t) by adding Q(s_t, a_t) to the difference between the TD-target and Q(s_t, a_t).

The TD-learning algorithm involves four main steps: calculating Q(s_t, a_t), going to the next state s_(t+1), calculating the TD-target, and updating Q(s_t, a_t).

Here are the steps of the TD-Learning algorithm in more detail:

The TD algorithm considers the temporal difference of Q(s,a) - the difference between two “versions” of Q(s, a) separated by time once before we take an action a in state s and once after that.

Deep Q-learning is a technique of deep reinforcement learning that programs AI agents to operate in environments with discrete actions spaces.

It's based on the Q-learning algorithm, but uses a deep neural network to approximate the optimal Q-function. This network outputs estimated Q-values for each action that can be taken from a given state.

The objective of this network is to approximate the optimal Q-function, which satisfies the Bellman equation. The loss from the network is calculated by comparing the outputted Q-values to the target Q-values from the Bellman equation.

The weights within the network are updated via SGD and backpropagation to minimize this loss. This process is repeated for each state in the environment until the loss is sufficiently minimized.

The deep Q-learning algorithm is trained over multiple time steps over many episodes, going through a sequence of operations in each time step. Experience Replay selects an ε-greedy action from the current state, executes it in the environment, and gets back a reward and the next state.

The network makes use of the Bellman equation to estimate the Q-values to find the optimal Q-function, similar to how we previously used the Bellman equation to compute and update Q-values in our Q-table.

Deep Q learning is a powerful tool, but it's not perfect. To address its limitations, we can use a Q-function instead of a Q-table.

A Q-function achieves the same result as a Q-table by mapping state and action pairs to a Q value, just like a Q-table does.

In real-world problems, using a Q-function can make a big difference. It allows us to tackle complex tasks that were previously out of reach.

By using a Q-function, we can create more efficient and effective solutions to real-world problems.