def test_single_training():
numberOfCells = 10 # in each axis
startingPosition = (4, 5) # head
foodPosition = (3, 6)
env = Environment(numberOfCells, deterministic=True)
agent = DQNAgent(state_size=env.state_size, action_size=Actions.action_size, deterministic=True, batch_size=24, memory_limit=2000)
state = env.reset(startingPosition, foodPosition)
agent.reset_convolutional_layers()
full_state = agent.get_convolutional_layers(state)
loss10 = -1
action10 = -1
maxsteps = 10
for step in range(maxsteps):
action = agent.get_exploration_action()
next_state, reward, done = env.step(action, food_position=(1, 1))
assert(not done)
full_next_state = agent.get_convolutional_layers(next_state)
assert(full_next_state.shape == (1, numberOfCells, numberOfCells, agent.numberOfLayers))
agent.save_transition(full_state, action, reward, full_next_state, done)
current_loss = agent.train()
full_state = full_next_state
loss10 = current_loss
action10 = action
assert(loss10 == 0.006804642267525196)
assert(action10 == 0)
def test_smoke():
# just runs the code - no assetions
numberOfCells = 10 # in each axis
startingPosition = (4, 5) # head
foodPosition = (3, 6)
env = Environment(numberOfCells)
agent = DQNAgent(state_size=env.state_size, action_size=Actions.action_size, deterministic=True, batch_size=24, memory_limit=2000)
state = env.reset(startingPosition, foodPosition)
agent.reset_convolutional_layers()
full_state = agent.get_convolutional_layers(state)
maxsteps = 2
for step in range(maxsteps):
action = agent.get_exploration_action()
next_state, reward, done = env.step(action, food_position=(1, 1))
full_next_state = agent.get_convolutional_layers(next_state)
assert(full_next_state.shape == (1, numberOfCells, numberOfCells, agent.numberOfLayers))
agent.save_transition(full_state, action, reward, full_next_state, done)
current_loss = agent.train()
if (step == 0):
action1 = action
loss1 = current_loss
full_state = full_next_state
loss2 = current_loss
action2 = action
def test_multiepisode_training():
numberOfCells = 10 # in each axis
startingPosition = (4, 5) # head
foodPosition = (3, 6)
env = Environment(numberOfCells, deterministic=True)
state_size = env.state_size
action_size = Actions.action_size # 3
agent = DQNAgent(state_size=state_size, action_size=action_size, deterministic=True, batch_size=24, memory_limit=2000)
losses = [-1, -1, -1, -1]
done = False
episodes = 4
maxsteps = 9
for e in range(episodes):
state = env.reset(startingPosition, foodPosition)
agent.reset_convolutional_layers()
full_state = agent.get_convolutional_layers(state)
loss = 0
for step in range(maxsteps):
action = agent.get_exploration_action()
next_state, reward, done = env.step(action, food_position=(1, 1)) # generation on (1, 1) happens once over the test
full_next_state = agent.get_convolutional_layers(next_state)
assert(full_next_state.shape == (1, numberOfCells, numberOfCells, agent.numberOfLayers))
agent.save_transition(full_state, action, reward, full_next_state, done)
current_loss = agent.train()
loss += current_loss
full_state = full_next_state
losses[e] = loss
assert(losses[0] == 3.9618697417899966)
assert(losses[1] == 0.044194952584803104)
assert(losses[2] == 0.1333141174982302)
assert(losses[3] == 2.834151452407241)