dqn算法matlab代码

以下是DQN算法的MATLAB代码示例：

%% Deep Q-Network (DQN) Algorithm
% Initialize replay memory D to capacity N
D = replay_memory(N);

% Initialize action-value function Q with random weights
Q = neural_network();

% Initialize target action-value function Q' with same weights as Q
Q_target = Q;

% For episode = 1, M do
for episode = 1:M
    % Initialize state s_1
    state = initial_state();
    
    % For t = 1, T do
    for t = 1:T
        % With probability e select a random action a_t
        if rand() < e
            action = random_action();
        % Otherwise select a_t = argmax_a Q(s_t, a; theta)
        else
            action = max_action(Q, state);
        end
        
        % Execute action a_t in emulator and observe reward r_t and
        % image x_{t+1}
        [reward, next_state] = emulator_step(action);
        
        % Store transition (s_t, a_t, r_t, s_{t+1}) in D
        D.store(state, action, reward, next_state);
        
        % Sample random minibatch of transitions (s_j, a_j, r_j, s_{j+1})
        minibatch = D.sample();
        
        % Set y_j = r_j for terminal s_{j+1} or y_j = r_j + gamma * max_a' Q_target(s_{j+1}, a'; theta') for non-terminal s_{j+1}
        y = calculate_targets(Q_target, minibatch, gamma);
        
        % Perform a gradient descent step on (y_j - Q(s_j, a_j; theta))^2 with respect to the network parameters theta
        Q = update_weights(Q, minibatch, y, alpha);
        
        % Every C steps reset Q_target = Q
        if mod(t, C) == 0
            Q_target = Q;
        end
        
        % Set s_t+1 = s_{t-1}
        state = next_state;
    end
end

请注意，以上是一个简单的DQN算法示例，并且有很多细节需要考虑，比如网络结构、采样策略、经验回放等。此外，为了使代码更加易于理解，我省略了一些细节。如果您想了解更多关于DQN算法的信息，请查看相关文献或参考开源代码库。