the environment is python3.9 and gym0.26
This Python script defines a Grid Environment suitable for Reinforcement Learning tasks. The environment is designed based on the OpenAI Gym framework, making it compatible with a variety of existing reinforcement learning algorithms.
import random
import sys
from typing import Optional
import pygame
import gym
class GridEnv(gym.Env):
...- State Space: The environment includes 8 possible states.
- Actions: The agent can perform 4 different actions - 'n', 'e', 's', 'w'.
- Reward Mechanism: Reward values are assigned to specific state-action pairs.
- State Transition: The transition function is defined in terms of state-action pairs, determining the next state after a given action is performed in a specific state.
- Terminal States: Three terminal states are defined. Once these states are reached, the episode ends.
- Visualisation: The environment can be rendered for visual inspection, using the Pygame library. It includes depictions of the agent, traps, and a goal state.
- Instantiate the environment.
env = GridEnv(render_mode='human')- Reset the environment before the start of each episode.
start = env.reset()- At each step of the episode, take an action. This will return the new state, reward, and a flag indicating if the episode has ended.
action = 'n' # or 'e', 's', 'w'
next_state, r, over,*_ =env.step(action)- Render the environment to visualize the state of the agent in the grid.
env.render()Please note that the provided code assumes certain image files (robot.png, trap.png, gold.png) are available in the working directory for rendering purposes.
For a complete understanding of the code, it is recommended to have familiarity with the gym environment interface and Python's dictionary data type for encoding reward and state transition mechanisms.