def run_expert(arguments, configuration):
agent_dir = Path(arguments.output_dir) / "expert"
if arguments.overwrite:
shutil.rmtree(arguments.output_dir, ignore_errors=True)
agent_dir.mkdir(parents=True, exist_ok=False)
#case
if arguments.case == 1:
ball_enabled_ = False
fire_enabled_ = True
else:
ball_enabled_ = False
fire_enabled_ = True
config = BreakoutConfiguration(
brick_rows=arguments.rows,
brick_cols=arguments.cols,
brick_reward=arguments.brick_reward,
step_reward=arguments.step_reward,
ball_enabled=ball_enabled_,
fire_enabled=fire_enabled_,
secondlearner=False) #ball_enabled=False, fire_enabled=True)
env = make_env(config, arguments.output_dir, arguments.goal_reward)
np.random.seed(arguments.seed)
env.seed(arguments.seed)
policy = AutomataPolicy((-2, ),
nb_steps=configuration.nb_exploration_steps,
value_max=1.0,
value_min=configuration.min_eps)
algorithm = Sarsa if configuration.algorithm == "sarsa" else QLearning
agent = Agent(algorithm(None,
env.action_space,
gamma=configuration.gamma,
alpha=configuration.alpha,
lambda_=configuration.lambda_),
policy=policy,
test_policy=EpsGreedyQPolicy(eps=0.01))
history = agent.fit(
env,
nb_steps=configuration.nb_steps,
visualize=configuration.visualize_training,
callbacks=[
ModelCheckpoint(str(agent_dir / "checkpoints" / "agent-{}.pkl")),
TrainEpisodeLogger()
])
history.save(agent_dir / "history.json")
agent.save(Path(agent_dir, "checkpoints", "agent.pkl"))
plot_history(history, agent_dir)
agent = Agent.load(agent_dir / "checkpoints" / "agent.pkl")
agent.test(Monitor(env, agent_dir / "videos"),
nb_episodes=5,
visualize=True)
env.close()
def run_learner(arguments, configuration, dfa: pythomata.dfa.DFA):
agent_dir = Path(arguments.output_dir) / "learner"
shutil.rmtree(agent_dir, ignore_errors=True)
agent_dir.mkdir(parents=True, exist_ok=False)
config = BreakoutConfiguration(brick_rows=arguments.rows,
brick_cols=arguments.cols,
brick_reward=arguments.brick_reward,
step_reward=arguments.step_reward,
fire_enabled=False,
ball_enabled=True)
env = make_env_from_dfa(config, dfa)
np.random.seed(arguments.seed)
env.seed(arguments.seed)
policy = AutomataPolicy((-1, ),
nb_steps=configuration.nb_exploration_steps,
value_max=0.8,
value_min=configuration.min_eps)
algorithm = Sarsa if configuration.algorithm == "sarsa" else QLearning
agent = Agent(algorithm(None,
env.action_space,
gamma=configuration.gamma,
alpha=configuration.alpha,
lambda_=configuration.lambda_),
policy=policy,
test_policy=EpsGreedyQPolicy(eps=0.001))
history = agent.fit(
env,
nb_steps=configuration.nb_steps,
visualize=configuration.visualize_training,
callbacks=[
ModelCheckpoint(str(agent_dir / "checkpoints" / "agent-{}.pkl")),
TrainEpisodeLogger()
])
history.save(agent_dir / "history.json")
agent.save(agent_dir / "checkpoints" / "agent.pkl")
plot_history(history, agent_dir)
agent = Agent.load(agent_dir / "checkpoints" / "agent.pkl")
agent.test(Monitor(env, agent_dir / "videos"),
nb_episodes=5,
visualize=True)
env.close()
def run_expert(arguments, configuration):
agent_dir = Path(arguments.output_dir) / "expert"
if arguments.overwrite:
shutil.rmtree(arguments.output_dir, ignore_errors=True)
agent_dir.mkdir(parents=True, exist_ok=False)
config = MinecraftConfiguration(horizon=100,
nb_goals=nb_tasks,
action_space_type=ActionSpaceType.NORMAL)
env = make_env(config,
arguments.output_dir,
arguments.goal_reward,
reward_shaping=configuration.reward_shaping)
np.random.seed(arguments.seed)
env.seed(arguments.seed)
policy = AutomataPolicy(tuple(-idx - 2 for idx in range(config.nb_goals)),
nb_steps=configuration.nb_exploration_steps,
value_max=1.0,
value_min=configuration.min_eps)
agent = Agent(Sarsa(None,
env.action_space,
gamma=configuration.gamma,
alpha=configuration.alpha,
lambda_=configuration.lambda_),
policy=policy,
test_policy=GreedyQPolicy())
history = agent.fit(
env,
nb_steps=configuration.nb_steps,
visualize=configuration.visualize_training,
callbacks=[
ModelCheckpoint(str(agent_dir / "checkpoints" / "agent-{}.pkl")),
TrainEpisodeLogger()
])
history.save(agent_dir / "history.json")
agent.save(Path(agent_dir, "checkpoints", "agent.pkl"))
plot_history(history, agent_dir)
agent = Agent.load(agent_dir / "checkpoints" / "agent.pkl")
agent.test(Monitor(env, agent_dir / "videos"),
nb_episodes=5,
visualize=True)
env.close()
def run_learner(arguments, configuration, dfa: pythomata.dfa.DFA):
agent_dir = Path(arguments.output_dir) / "learner"
shutil.rmtree(agent_dir, ignore_errors=True)
agent_dir.mkdir(parents=True, exist_ok=False)
config = MinecraftConfiguration(
horizon=100,
nb_goals=1,
action_space_type=ActionSpaceType.DIFFERENTIAL)
env = make_env_from_dfa(config, dfa, goal_reward=arguments.goal_reward)
np.random.seed(arguments.seed)
env.seed(arguments.seed)
policy = AutomataPolicy((-1, ),
nb_steps=configuration.nb_exploration_steps,
value_max=1.0,
value_min=configuration.min_eps)
algorithm = Sarsa if configuration.algorithm == "sarsa" else QLearning
agent = Agent(algorithm(None,
env.action_space,
gamma=configuration.gamma,
alpha=configuration.alpha,
lambda_=configuration.lambda_),
policy=policy,
test_policy=GreedyQPolicy())
history = agent.fit(
env,
nb_steps=configuration.nb_steps,
visualize=configuration.visualize_training,
callbacks=[
ModelCheckpoint(str(agent_dir / "checkpoints" / "agent-{}.pkl")),
TrainEpisodeLogger()
])
history.save(agent_dir / "history.json")
agent.save(agent_dir / "checkpoints" / "agent.pkl")
plot_history(history, agent_dir)
agent = Agent.load(agent_dir / "checkpoints" / "agent.pkl")
agent.test(Monitor(env, agent_dir / "videos"),
nb_episodes=5,
visualize=True)
env.close()