how to code reinforcement learning in matlab? in matlab

To code reinforcement learning in MATLAB, we can use the Q-Learning algorithm as an example. Q-Learning is a model-free, online, off-policy reinforcement learning method that learns to predict the value of a state-action pair.

First, we need to initialize the Q-table, which is a matrix of state-action pairs, with random values. We also need to set up the parameters, such as alpha (learning rate), gamma (discount factor), and epsilon (exploration rate).

main.m
Q = rand(states, actions);
alpha = 0.1;
gamma = 0.9;
epsilon = 0.1;
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Next, we need to define the reward function and the transition function. In this example, we use a grid world environment where the agent can move up, down, left, or right. The reward is +1 for reaching the goal, -1 for falling into a hole, and 0 for all other states.

main.m
reward = zeros(states, actions);
reward(goal_state, :) = 1;
reward(hole_state, :) = -1;

transition = zeros(states, actions, states);
transition(:,:,goal_state) = 1;
transition(:,:,hole_state) = 1;
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Then, we can run the Q-Learning algorithm by iterating over episodes and steps. At each step, we select an action based on the epsilon-greedy policy and update the Q-table using the Bellman equation.

main.m
for i = 1:episodes
    s = start_state;
    for j = 1:steps
        if rand < epsilon
            a = randi(actions);
        else
            [~, a] = max(Q(s,:));
        end
        s_next = find(rand < cumsum(transition(s,a,:)), 1);
        q_target = reward(s,a) + gamma * max(Q(s_next,:));
        Q(s,a) = Q(s,a) + alpha * (q_target - Q(s,a));
        s = s_next;
    end
end
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Finally, we can use the Q-table to make predictions and select actions in a new environment.

main.m
s = start_state;
while s ~= goal_state && s ~= hole_state
    [~, a] = max(Q(s,:));
    s = find(rand < cumsum(transition(s,a,:)), 1);
end
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