The Q-learning algorithm is a model-free, online, off-policy reinforcement learning method. A Q-learning agent is a value-based reinforcement learning agent that trains a critic to estimate the return or future rewards.
For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.
Q-learning agents can be trained in environments with the following observation and action spaces.
|Observation Space||Action Space|
|Continuous or discrete||Discrete|
Q agents use the following critic representation.
Q-value function critic Q(S,A), which you create using
Q agents do not use an actor.
During training, the agent explores the action space using epsilon-greedy exploration. During each control interval the agent selects a random action with probability ϵ, otherwise it selects an action greedily with respect to the value function with probability 1-ϵ. This greedy action is the action for which the value function is greatest.
To estimate the value function, a Q-learning agent maintains a critic Q(S,A), which is a table or function approximator. The critic takes observation S and action A as inputs and returns the corresponding expectation of the long-term reward.
For more information on creating critics for value function approximation, see Create Policy and Value Function Representations.
When training is complete, the trained value function approximator is stored in critic Q(S,A).
To create a Q-learning agent:
Q-learning agents use the following training algorithm. To configure the training
algorithm, specify options using an
Initialize the critic Q(S,A) with random values.
For each training episode:
Set the initial observation S.
Repeat the following for each step of the episode until S is a terminal state.
For the current observation S, select a random action A with probability ϵ. Otherwise, select the action for which the critic value function is greatest.
To specify ϵ and its decay rate, use the
Execute action A. Observe the reward R and next observation S'.
If S' is a terminal state, set the value function target y to R. Otherwise, set it to
To set the discount factor γ, use the
Compute the critic parameter update.
Update the critic using the learning rate α.
Specify the learning rate when you create the critic representation by
LearnRate option in the
Set the observation S to S'.