def run_maze_learner(mission, clients):
if 'malmopy.visualization.tensorboard' in sys.modules:
visualizer = TensorboardVisualizer()
visualizer.initialize(logdir, None)
else:
visualizer = ConsoleVisualizer()
# with TensorboardVisualizer() as visualizer:
env = MazeEnvironment(mission,
[str.split(client, ':') for client in clients])
env.recording = False
# explorer = LinearEpsilonGreedyExplorer(1, 0.1, 10000)
# model = DeepQNeuralNetwork((4, 84, 84), (env.available_actions,), momentum=0, visualizer=visualizer)
# memory = TemporalMemory(50000, model.input_shape[1:], model.input_shape[0], False)
agent = RandomAgent(
"rand", 3
) #DQNAgent("Maze DQN Agent", env.available_actions, model, memory, explorer=explorer,
#visualizer=visualizer)
# exp = SingleAgentExperiment("Malmo Cliff Walking", agent, env, 500000, warm_up_timesteps=500,
# visualizer=visualizer)
# exp.episode_end += on_episode_end
# visualizer.initialize(MALMO_MAZE_FOLDER, model, CntkConverter())
# with Popen(['tensorboard', '--logdir=%s' % path.join(MALMO_MAZE_FOLDER, path.pardir), '--port=6006']):
EPOCH_SIZE = 250000
max_training_steps = 50 * EPOCH_SIZE
state = env.reset()
reward = 0
agent_done = False
viz_rewards = []
for step in range(1, max_training_steps + 1):
# action = agent.act(state, reward, agent_done, is_training=True)
# check if env needs reset
if env.done:
visualize_training(visualizer, step, viz_rewards)
agent.inject_summaries(step)
viz_rewards = []
state = env.reset()
# select an action
action = agent.act(state, reward, agent_done, is_training=True)
print('ACTION BEING TAKEN: ', action)
# take a step
state, reward, agent_done = env.do(action)
viz_rewards.append(reward)
if (step % EPOCH_SIZE) == 0:
model.save('%s-%s-dqn_%d.model' %
(backend, environment, step / EPOCH_SIZE))
def run_maze_learner(mission, clients):
if 'malmopy.visualization.tensorboard' in sys.modules:
visualizer = TensorboardVisualizer()
visualizer.initialize(logdir, None)
else:
visualizer = ConsoleVisualizer()
env = MazeEnvironment(mission,
[str.split(client, ':') for client in clients])
env.recording = False
agent = RandomAgent("rand", 3, delay_between_action=1.5)
#taking random actions
EPOCH_SIZE = 250000
max_training_steps = 50 * EPOCH_SIZE
state = env.reset()
reward = 0
agent_done = False
viz_rewards = []
for step in range(1, max_training_steps + 1):
action = agent.act(state, reward, agent_done, is_training=True)
# check if env needs reset
if env.done:
visualize_training(visualizer, step, viz_rewards)
agent.inject_summaries(step)
viz_rewards = []
state = env.reset()
# select an action
action = agent.act(state, reward, agent_done, is_training=True)
print('ACTION BEING TAKEN: ', action)
# take a step
state, reward, agent_done = env.do(action)
viz_rewards.append(reward)
if (step % EPOCH_SIZE) == 0:
model.save('%s-%s-dqn_%d.model' %
(backend, environment, step / EPOCH_SIZE))