# Reinforcement Learning Project1 Report

## Project 1: Navigation

### DQN
The main idea of training this navigating agent is DQN.

### Model
The Q network we implement here is simple, only consists of three fully connected layers.

import torch
import torch.nn as nn
import torch.nn.functional as F

class QNetwork(nn.Module):
    """Actor (Policy) Model."""

    def __init__(self, state_size, action_size, seed, fc1_units = 64, fc2_units = 64):
        """Initialize parameters and build model.
        Params
        ======
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            seed (int): Random seed
        """
        super(QNetwork, self).__init__()
        self.seed = torch.manual_seed(seed)
        "*** YOUR CODE HERE ***"
        self.seed = torch.manual_seed(seed)
        self.fc1 = nn.Linear(state_size, fc1_units)
        self.fc2 = nn.Linear(fc1_units, fc2_units)
        self.fc3 = nn.Linear(fc2_units, action_size)

    def forward(self, state):
        """Build a network that maps state -> action values."""
        x = F.relu(self.fc1(state))
        x = F.relu(self.fc2(x))
        return self.fc3(x)

### Results
The learning curve within 500 episodes show scores increasing.

### Future work
#### 1. Try double DQN
#### 2. Try learning on Pixels