def test_nn_runner_run_batch_agent(benchmark):
#Run batch against randomly initialized opponents
agent = Agent(base_net_file=None)
opponent = Agent(base_net_file=None)
game_runner = GameRunner(opponent=opponent)
nnrunner = NNRunner(agent,game_runner)
benchmark(nnrunner.run_batch,1)
def test_nn_runner_run_batch_random(benchmark):
#Run batch against random opponent
agent = Agent(base_net_file=None)
opponent = RandomAgent()
game_runner = GameRunner(opponent=opponent)
nnrunner = NNRunner(agent,game_runner)
benchmark(nnrunner.run_batch,1)
def test_nn_runner_train_1_1(benchmark):
#Benchmark test for training with one
agent=Agent()
opponent=Agent()
game_runner=GameRunner(opponent=opponent)
nnrunner = NNRunner(agent,game_runner)
benchmark.pedantic(nnrunner.train,kwargs={"batch_size":1,"batches":1},rounds=10)
def test_nn_runner_run_episode():
agent=Agent()
game_runner=GameRunner()
nnrunner = NNRunner(agent,game_runner)
rewards, values, log_probs, entropy_term = nnrunner.run_episode()
assert len(rewards) == len(values) == len(log_probs) == len(entropy_term)
assert len(rewards) > 0
def test_nn_runner_run_batch():
agent=Agent()
game_runner=GameRunner()
nnrunner = NNRunner(agent,game_runner)
#Test that training with different batch sizes are possible
nnrunner.run_batch(episodes=1)
nnrunner.run_batch(episodes=2)
#Test that training works with a random agent
opponent=RandomAgent()
game_runner=GameRunner(opponent=opponent)
nnrunner = NNRunner(agent,game_runner)
nnrunner.run_batch(episodes=2)
#Test that training works with a default agent
opponent=Agent()
game_runner=GameRunner(opponent=opponent)
nnrunner = NNRunner(agent,game_runner)
nnrunner.run_batch(episodes=2)
def test_init():
gamerunner = GameRunner()
#Move counter should be set to 0
assert gamerunner.move_counter == 0
#Player should start with 0 score
assert gamerunner.player_score == 0
#Game board should be set
game_board = gamerunner.game.game_board_displays
for i in range(5):
assert np.sum(game_board[i])==4
game_center = gamerunner.game.game_board_center
assert np.array_equal(game_center,np.array([0,0,0,0,0,1]))
def test_init():
agent=Agent()
game_runner=GameRunner()
nnrunner = NNRunner(agent,game_runner)
def test_get_state():
gamerunner = GameRunner()
state_flat = gamerunner.get_state()
assert np.sum(state_flat) == 4*5 + 1
assert np.size(state_flat) == 5*5 + 6 + 5*5*2 + 5*5*2 + 2 + 2 + 1
def test_step():
#Use seed 1 to get a state equal to the test resource "first round"
random.seed(1)
gamerunner = GameRunner()
reward, end_of_game = gamerunner.step(nn_serialize(1,0,2))
#The turn should have gone back to the original player
assert gamerunner.game.current_player == 1
#For this seed, game should not have ended after one step
assert not end_of_game
#Score on the original board should not have been counted
assert np.array_equal(gamerunner.game.score,np.zeros(2))
#Load game_end_of_round_2 to see that game_board gets reset and player gets back to one
gamerunner = GameRunner()
gamerunner.game.import_JSON(script_dir+"/resources/game_end_of_round_2.json")
reward, end_of_game = gamerunner.step(nn_serialize(0,4,1))
assert gamerunner.game.current_player == 1
#For this scenario, game should have ended after one step
assert not end_of_game
#Load game_end_of_round_2 to see that the game ends if the right steps are made
gamerunner = GameRunner()
gamerunner.game.import_JSON(script_dir+"/resources/game_end_of_round_2.json")
gamerunner.player_score=49-32
random.seed(1)
reward, end_of_game = gamerunner.step(nn_serialize(0,0,1))
assert end_of_game
#Check that player score is same as score difference
assert gamerunner.player_score==gamerunner.game.score[0]-gamerunner.game.score[1]
#Load game_end_of_round_3 to see that game_board gets reset
gamerunner = GameRunner()
gamerunner.game.import_JSON(script_dir+"/resources/game_end_of_round_3.json")
gamerunner.player_score=49-32
reward, end_of_game = gamerunner.step(nn_serialize(0,3,0))
#For this scenario, game should not have ended after one step
assert not end_of_game
#With this scenario, reward should be -6
assert reward == -6
#Since board is reset, the player_score should be equal to the game score difference
assert gamerunner.player_score==gamerunner.game.score[0]-gamerunner.game.score[1]
#Game board should be set
game_board = gamerunner.game.game_board_displays
for i in range(5):
assert np.sum(game_board[i])==4
game_center = gamerunner.game.game_board_center
assert np.array_equal(game_center,np.array([0,0,0,0,0,1]))
assert gamerunner.game.current_player == 1