def step(snakes: list, food: Food, action):
# First snake must be the player7
player = snakes[0]
player.action([action], "AC")
done = False
obs = "Wrong Input"
# currently overall reward increases every time food is eaten
handleFoodEating(snakes, food)
# the current state after move has been done
# could be the whole f*****g grid or maybe just part of it around snakes head
# for now let me try making it a 5x5 grid around its head
if obs_type == "Grid":
obs = getObsGrid(snakes, food, OBS_GRID_SIZE, fullGrid=False)
if obs_type == "Small":
obs = getObsSmall(snakes, food)
info = ""
return obs, player.reward, done, info
return obs, player.reward, done, info
score_history = []
score = 0
n_steps_history = []
for i in tqdm(range(num_episodes)):
player = Snake(0)
enemy = Snake(1)
enemy.positions = [(random.randint(0, SIZE - 1), random.randint(0, SIZE - 1))]
food = Food([player])
done = False
score = 0
# returns a numpy array of the state we care about
observation = getObsGrid(snakes=[player], food=food, size=OBS_GRID_SIZE, fullGrid=False)
#observation = getObsSmall([player, enemy], food)
n_steps = 0
while not done and n_steps < 100:
n_steps += 1
# action needs to be either 0,1,2 or 3
action = agentAC.choose_action(observation)
observation_, reward, done, info = step(snakes=[player, enemy], food=food, action=action, obs_type=obs_type)
agentAC.learn(observation, reward, observation_, done) # For Actor-Critic
#agent.store_rewards(reward) # For REINFORCE
observation = observation_
score += reward
score_history.append(score)
n_steps_history.append(n_steps)
if i % SHOW_EVERY == 0:
#print(f"on #{i}, epsilon is {lr}")