def main():
#Creates a log for MineRL
#logging.basicConfig(level=logging.DEBUG)
# Create the environment
ENV_NAME = "MineRLTreechop-v0"
# Pre-defined or custom environment
env = gym.make(ENV_NAME)
environment = OpenAIGym(env)
agent = Agent.create(agent='ac',
environment=environment,
max_episode_timesteps=8000,
exploration=.03,
critic_optimizer='evolutionary')
sum_rewards = 0.0
rewards_by_episode = []
for _ in range(200):
states = environment.reset()
terminal = False
print("Training episode " + str(_))
while not terminal:
actions = agent.act(states=states, evaluation=True)
states, terminal, reward = environment.execute(actions=actions)
sum_rewards += reward
#print(actions)
print("Sum reward so far: " + str(sum_rewards))
rewards_by_episode.append((_, sum_rewards))
print("Ending episode ", _)
print(rewards_by_episode)
print('Mean episode reward:', sum_rewards / 200)
agent.close()
environment.close()
learning_rate=1e-3,
name='agent_loader'
)
# import ipdb;ipdb.set_trace()
agent = agent.load()
running_score = 0.0
# Train for 300 episodes
for i_epoch in range(50000):
game_score = 0.0
# Initialize episode
states = environment.reset()
terminal = False
while not terminal:
# Episode timestep
actions = agent.act(states=states,evaluation=True)
states, terminal, reward = environment.execute(actions=actions)
game_score+=reward
# agent.observe(terminal=terminal, reward=reward)
running_score = 0.95*running_score + 0.05*game_score
if i_epoch%5==0:
print("Game ", i_epoch, " game score %.2f"%game_score," running score %.2f"%running_score)
agent.close()
environment.close()