使用 Python、MATLAB 和 Julia 实现 DQN 算法

深度 Q 学习 (DQN) 是一种流行的强化学习算法，它使用神经网络来学习最优策略。在本篇文章中，我们将展示如何使用三种编程语言 Python、MATLAB 和 Julia 实现 DQN 算法。

Python

import numpy as np
import tensorflow as tf
from tensorflow import keras

# 定义 DQN 类
class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = []
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.01
        self.learning_rate = 0.001
        self.model = self.build_model()

    # 定义神经网络模型
    def build_model(self):
        model = keras.Sequential()
        model.add(keras.layers.Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(keras.layers.Dense(24, activation='relu'))
        model.add(keras.layers.Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=keras.optimizers.Adam(lr=self.learning_rate))
        return model

    # 将状态、动作、奖励、下一状态和完成标志添加到记忆中
    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    # 根据当前状态选择动作
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        else:
            return np.argmax(self.model.predict(state)[0])

    # 使用随机采样的记忆批次训练神经网络
    def replay(self, batch_size):
        if len(self.memory) < batch_size:
            return
        minibatch = np.random.choice(self.memory, batch_size, replace=False)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = reward + self.gamma * np.amax(self.model.predict(next_state)[0])
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

# 定义环境和智能体
env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
agent = DQNAgent(state_size, action_size)

# 训练智能体
batch_size = 32
num_episodes = 1000
for episode in range(num_episodes):
    state = env.reset()
    state = np.reshape(state, [1, state_size])
    done = False
    for time_step in range(500):
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        reward = reward if not done else -10
        next_state = np.reshape(next_state, [1, state_size])
        agent.remember(state, action, reward, next_state, done)
        state = next_state
        if done:
            print('episode: {}/{}, score: {}, epsilon: {:.2}'.format(episode, num_episodes, time_step, agent.epsilon))
            break
    agent.replay(batch_size)

MATLAB

classdef DQNAgent < handle
    properties
        state_size
        action_size
        memory = []
        gamma = 0.95
        epsilon = 1.0
        epsilon_decay = 0.995
        epsilon_min = 0.01
        learning_rate = 0.001
        model
    end
    methods
        function obj = DQNAgent(state_size, action_size)
            obj.state_size = state_size;
            obj.action_size = action_size;
            obj.model = obj.build_model();
        end
        
        function model = build_model(obj)
            model = rlSimpleNetwork([obj.state_size, 24, 24, obj.action_size], ...
                [reluLayer, reluLayer, linearLayer], ...
                'Observation', {'input'}, ...
                'Action', {'rlAction'}, ...
                'LearningRate', obj.learning_rate, ...
                'Name', 'DQN');
            model.Options.GradientThreshold = 1;
            model.Options.UseDevice = 'gpu';
            model.Options.ExecutionEnvironment = 'gpu';
            model.Options.EpsilonGreedyExploration.Epsilon = obj.epsilon;
        end
        
        function remember(obj, state, action, reward, next_state, done)
            obj.memory = [obj.memory; {state, action, reward, next_state, done}];
        end
        
        function action = act(obj, state)
            if rand < obj.epsilon
                action = randi([1, obj.action_size]);
            else
                action = predict(obj.model, state);
            end
        end
        
        function replay(obj, batch_size)
            if size(obj.memory, 1) < batch_size
                return;
            end
            minibatch = datasample(obj.memory, batch_size, 'Replace', false);
            for i = 1:batch_size
                state = minibatch{i, 1};
                action = minibatch{i, 2};
                reward = minibatch{i, 3};
                next_state = minibatch{i, 4};
                done = minibatch{i, 5};
                target = reward;
                if ~done
                    target = reward + obj.gamma * max(predict(obj.model, next_state));
                end
                target_f = predict(obj.model, state);
                target_f(action) = target;
                train(obj.model, state, target_f);
            end
            if obj.epsilon > obj.epsilon_min
                obj.epsilon = obj.epsilon * obj.epsilon_decay;
                obj.model.Options.EpsilonGreedyExploration.Epsilon = obj.epsilon;
            end
        end
    end
end

env = rlPredefinedEnv('CartPole-Discrete');
state_size = env.ObservationInfo.Dimension(1);
action_size = env.ActionInfo.Dimension(1);
agent = DQNAgent(state_size, action_size);

batch_size = 32;
num_episodes = 1000;
for episode = 1:num_episodes
    state = reset(env);
    done = false;
    for time_step = 1:500
        action = act(agent, state);
        [next_state, reward, done, ~] = step(env, action);
        reward = reward * (1 - double(done));
        remember(agent, state, action, reward, next_state, done);
        state = next_state;
        if done
            fprintf('episode: %d/%d, score: %d, epsilon: %.2f\n', episode, num_episodes, time_step, agent.epsilon);
            break
        end
    end
    replay(agent, batch_size);
end

Julia

using Flux, Random, Statistics, Base.Iterators, ReinforcementLearningEnvironments

struct DQNAgent
    state_size::Int
    action_size::Int
    memory::Vector{Tuple{Vector{Float32}, Int64, Float32, Vector{Float32}, Bool}}
    gamma::Float32
    epsilon::Float32
    epsilon_decay::Float32
    epsilon_min::Float32
    learning_rate::Float32
    model::Chain
end

function DQNAgent(state_size::Int, action_size::Int)
    model = Chain(Dense(state_size, 24, relu), Dense(24, 24, relu), Dense(24, action_size))
    model = gpu(model)
    optimizer = ADAM(learning_rate=0.001)
    return DQNAgent(state_size, action_size, [], 0.95f0, 1.0f0, 0.995f0, 0.01f0, 0.001f0, model)
end

function remember(agent::DQNAgent, state::Vector{Float32}, action::Int64, reward::Float32, next_state::Vector{Float32}, done::Bool)
    push!(agent.memory, (state, action, reward, next_state, done))
end

function act(agent::DQNAgent, state::Vector{Float32})
    if rand(Float32) < agent.epsilon
        return rand(1:agent.action_size)
    else
        return argmax(Flux.gpu(agent.model)(state))
    end
end

function replay(agent::DQNAgent, batch_size::Int64)
    if length(agent.memory) < batch_size
        return
    end
    minibatch = sample(agent.memory, batch_size, replace=false)
    for (state, action, reward, next_state, done) in minibatch
        target = reward
        if !done
            target = reward + agent.gamma * maximum(Flux.gpu(agent.model)(next_state))
        end
        target_f = Flux.gpu(agent.model)(state)
        target_f[action] = target
        Flux.train!(loss_fn(state, target_f, agent.model), params(agent.model), minibatch, optimizer=agent.optimizer)
    end
    if agent.epsilon > agent.epsilon_min
        agent.epsilon = agent.epsilon * agent.epsilon_decay
    end
end

function loss_fn(state::Vector{Float32}, target_f::Vector{Float32}, model::Chain)
    loss = Flux.Losses.mse(Flux.gpu(model)(state), target_f)
    return loss
end

env = CartPoleEnv();
state_size = length(env.observation_space.low)
action_size = env.action_space.n
agent = DQNAgent(state_size, action_size)

batch_size = 32
num_episodes = 1000
for episode in 1:num_episodes
    state = reset!(env)
    done = false
    for time_step in 1:500
        action = act(agent, state)
        next_state, reward, done, _ = step!(env, action)
        remember(agent, state, action, reward, next_state, done)
        state = next_state
        if done
            println('episode: $episode/$num_episodes, score: $time_step, epsilon: $(agent.epsilon)')
            break
        end
    end
    replay(agent, batch_size)
end

总结

这篇文章展示了如何使用 Python、MATLAB 和 Julia 实现 DQN 算法。每个代码示例都包含了构建模型、训练模型和评估模型的基本步骤。您可以在这些代码的基础上进行修改和扩展，以应用于不同的强化学习问题。

希望这篇文章对您有所帮助！如果您有任何问题，请随时提出。