Deep Reinforcement Learning | Deep Learning

Introduction

Deep Reinforcement Learning (DRL) combines reinforcement learning (RL) and deep learning (DL) to enable agents to make decisions from high-dimensional sensory inputs. In DRL, an agent learns to achieve its goals by interacting with an environment, receiving feedback, and adjusting its strategies accordingly.

Key Concepts

Agent: The learner or decision-maker.
Environment: The external system the agent interacts with.
State: A representation of the current situation of the environment.
Action: A set of all possible moves the agent can make.
Reward: Feedback from the environment based on the action taken.
Policy: A strategy employed by the agent to determine actions based on states.

Note: The goal of DRL is to maximize the cumulative reward over time by learning an optimal policy.

Workflow


graph TD;
    A[Start] --> B[Initialize agent and environment];
    B --> C[Observe current state];
    C --> D[Select action using policy];
    D --> E[Receive reward and next state];
    E --> F[Update policy based on reward];
    F --> C;
    F -->|End| G[Terminate if goal is reached];

Implementation

Below is a simplified example of implementing a Deep Reinforcement Learning agent using Python and TensorFlow.


import numpy as np
import tensorflow as tf

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  # discount rate
        self.epsilon = 1.0  # exploration rate
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.model = self._build_model()

    def _build_model(self):
        model = tf.keras.Sequential()
        model.add(tf.keras.layers.Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(tf.keras.layers.Dense(24, activation='relu'))
        model.add(tf.keras.layers.Dense(self.action_size, activation='linear'))
        model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.Adam(lr=0.001))
        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])  # returns action
    
    def replay(self, batch_size):
        minibatch = np.random.choice(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target += self.gamma * np.max(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)

FAQ

What is the difference between supervised learning and reinforcement learning?

Supervised learning uses labeled data to train models, while reinforcement learning uses feedback from the environment to learn optimal actions.

What are some applications of Deep Reinforcement Learning?

Applications include game playing (like AlphaGo), robotics, autonomous vehicles, and recommendation systems.

How does exploration vs exploitation work in DRL?

Exploration involves trying new actions to discover their effects, while exploitation involves using known actions that yield the best rewards.