Reinforcement Learning Agent for Grid Navigation

This challenge involves building a reinforcement learning agent that can learn to navigate a simple grid environment. The goal is to train an agent to find a path from a starting point to a goal point while avoiding obstacles. This is a fundamental problem in reinforcement learning, demonstrating core concepts like states, actions, rewards, and learning policies.

Problem Description

You are tasked with implementing a reinforcement learning agent that can learn to navigate a 2D grid. The agent starts at a specified position and must reach a designated goal position. The grid can contain walls (obstacles) that the agent cannot pass through. The agent learns by trial and error, receiving positive rewards for reaching the goal and negative rewards for hitting walls or taking too long.

Key Requirements:

Environment Representation: Define a grid environment where the agent can move. This environment should represent the agent's position, the goal's position, and the locations of any obstacles.
Agent Actions: The agent should be able to perform a set of discrete actions, such as "move up," "move down," "move left," and "move right."
State Representation: The agent's current position in the grid will serve as its state.
Reward System: Design a reward function that incentivizes reaching the goal and penalizes undesirable actions (e.g., hitting a wall, exceeding a step limit).
Reinforcement Learning Algorithm: Implement a basic reinforcement learning algorithm, such as Q-learning, to enable the agent to learn an optimal policy.
Policy Extraction: After training, the agent should be able to provide a sequence of actions to reach the goal from any valid starting position within the trained environment.

Expected Behavior:

After sufficient training episodes, the agent's learned policy should guide it to the goal efficiently, avoiding obstacles. When presented with a starting position, the agent should output a sequence of moves that leads to the goal.

Edge Cases to Consider:

No Path to Goal: The environment might be designed such that there is no valid path from the start to the goal. The agent should ideally learn to recognize this or repeatedly try without reaching the goal, indicating a failure.
Agent Starting at Goal: The agent should immediately recognize it has reached the goal and require no actions.
Agent Starting on an Obstacle: This should ideally be prevented by design or handled with a specific penalty.

Examples

Example 1:

A simple 3x3 grid.

Grid:
[['S', '.', '.'],
 ['.', '#', '.'],
 ['.', '.', 'G']]

Start: (0, 0)
Goal: (2, 2)
Obstacle: (1, 1)

Expected Output (Learned Policy from (0,0)): A sequence of moves like ['right', 'right', 'down', 'down'] or ['down', 'down', 'right', 'right']. The exact sequence might vary slightly based on the RL algorithm's exploration/exploitation trade-off and tie-breaking.

Explanation: The agent needs to move right twice and down twice to reach the goal, navigating around the obstacle at (1,1).

Example 2:

A 4x4 grid with a more complex path.

Grid:
[['S', '.', '.', '.'],
 ['#', '#', '.', '#'],
 ['.', '.', '.', '.'],
 ['.', '#', 'G', '.']]

Start: (0, 0)
Goal: (3, 2)
Obstacles: (1, 0), (1, 1), (1, 3), (3, 1)

Expected Output (Learned Policy from (0,0)): A sequence of moves like ['right', 'right', 'down', 'down', 'right'].

Explanation: The agent must find a path through the open areas, avoiding the walls.

Example 3: (Edge Case - No Path)

A 3x3 grid where the goal is unreachable.

Grid:
[['S', '.', '#'],
 ['.', '#', '#'],
 ['.', '.', 'G']]

Start: (0, 0)
Goal: (2, 2)
Obstacles: (0, 2), (1, 1), (1, 2)

Expected Output (Learned Policy from (0,0)): After training, if the agent is asked to find a path, it might output an empty list or a very long sequence of moves indicating it couldn't find the goal. For this challenge, returning an empty list or a signal indicating failure to reach the goal within a reasonable number of steps is acceptable.

Explanation: The goal is completely blocked by walls, making it impossible to reach from the start.

Constraints

Grid dimensions will be between 3x3 and 10x10.
The number of obstacles will not exceed 30% of the total grid cells.
There will be exactly one start ('S') and one goal ('G') position.
Obstacles are represented by '#'.
Empty traversable cells are represented by '.'.
The agent's actions are limited to 'up', 'down', 'left', 'right'.
The RL algorithm should converge within a reasonable number of training episodes (e.g., 1000-10000 episodes) for typical grid sizes.

Notes

You can use libraries like NumPy for grid representation and management.
For the RL algorithm, consider Q-learning. You'll need to define a Q-table (or a dictionary-based approach) to store Q-values for state-action pairs.
Choose appropriate learning rate (alpha), discount factor (gamma), and exploration rate (epsilon) for your Q-learning implementation. You might want to implement epsilon-greedy exploration and decay epsilon over time.
The reward function is critical. A common setup is:
- +10 for reaching the goal.
- -10 for hitting a wall.
- -0.1 for each step taken (to encourage efficiency).
Consider a maximum number of steps per episode to prevent infinite loops, with a penalty for exceeding this limit.
After training, you'll want a function to extract the greedy policy (i.e., choosing the action with the highest Q-value for a given state).