dqn算法matlab代码

以下是DQN算法的Matlab代码，用于实现深度强化学习的智能体训练:

% DQN Algorithm

% Initialize replay memory D
D = [];

% Initialize Q-network with random weights
Q = randn(inputSize, numActions);

% Initialize target Q-network with same weights as Q-network
QTarget = Q;

% Initialize action selection parameters
epsilon = 1; % exploration rate
minEpsilon = 0.1; % minimum exploration rate
epsilonDecayRate = 0.0001; % exploration rate decay rate

% Initialize other hyperparameters
batchSize = 32; % minibatch size
gamma = 0.99; % discount factor
maxSteps = 10000; % maximum number of steps per episode

% Start training
for episode = 1:numEpisodes
    % Initialize state
    state = env.reset();
    
    % Initialize episode variables
    totalReward = 0;
    step = 0;
    
    % Loop over steps in episode
    while step < maxSteps
        % Choose action according to epsilon-greedy policy
        if rand() < epsilon
            action = randi(numActions);
        else
            [~, action] = max(Q(state,:));
        end
        
        % Take action and observe next state and reward
        [nextState, reward, done] = env.step(action);
        
        % Store transition in replay memory
        D = [D; state, action, reward, nextState, done];
        
        % Update state and total reward
        state = nextState;
        totalReward = totalReward + reward;
        
        % Sample minibatch from replay memory
        minibatch = D(randi(size(D,1),batchSize,1),:);
        
        % Compute target Q-values
        targetQ = minibatch(:,3) + gamma*max(QTarget(minibatch(:,4),:),[],2).*(~minibatch(:,5));
        
        % Compute predicted Q-values
        predictedQ = Q(minibatch(:,1),minibatch(:,2));
        
        % Compute loss and update Q-network
        loss = mean((targetQ - predictedQ).^2);
        Q(minibatch(:,1),minibatch(:,2)) = Q(minibatch(:,1),minibatch(:,2)) + alpha*(targetQ - predictedQ);
        
        % Update target Q-network every C steps
        if mod(step,C) == 0
            QTarget = Q;
        end
        
        % Decay exploration rate
        epsilon = max(minEpsilon, epsilon - epsilonDecayRate);
        
        % Check if episode is done
        if done
            break;
        end
        
        % Increment step counter
        step = step + 1;
    end
    
    % Print episode statistics
    fprintf('Episode %d: total reward = %.2f, epsilon = %.2f\n', episode, totalReward, epsilon);
end

请注意，这只是一个示例代码，您需要根据您自己的环境和要解决的问题进行适当修改。