def test_dqn_1(self):
def on_step_end(agent, reward, observation, done, action):
"""Callback to print stuff to console"""
if agent.total_step % 1000 == 0:
print('test_dqn_1', agent.total_step)
if done:
print('episode terminated at', agent.total_step)
env = gym.make('MountainCar-v0').env # .env to remove 200 step limit
env.seed(self.seed)
#
# Keras DQN model
#
q_model = tf.keras.models.Sequential()
q_model.add(
tf.keras.layers.Dense(units=256, activation='relu', input_dim=2))
q_model.add(tf.keras.layers.Dense(units=256, activation='relu'))
q_model.add(tf.keras.layers.Dense(units=3, activation='linear'))
q_model.compile(loss='mse',
optimizer=tf.keras.optimizers.RMSprop(lr=0.00025))
# Configure agent
agent = rl.AgentDQN(state_space=env.observation_space,
action_space=env.action_space,
discount=0.99,
start_learning_at=22000,
memory=rl.MemoryDQN(max_len=10000,
batch_size=64,
enable_pmr=False,
initial_pmr_error=1000.0),
q_fun_approx=rl.QFunctKeras(model=q_model),
policy=rl.PolicyEpsGreedy(expl_start=False,
nb_rand_steps=22000,
e_rand_start=1.0,
e_rand_target=0.1,
e_rand_decay=1 / 10000))
agent.register_callback('on_step_end', on_step_end)
# Main train loop
rl.train_agent(env=env, agent=agent, total_steps=23000)
# This is used to test for any numerical discrepancy between runs
fp, ws, st, act, rew, done = agent.get_fingerprint()
print('FINGERPRINT:', fp)
print(' wegight sum:', ws)
print(' st, act, rew, done:', st, act, rew, done)
self.assertEqual(fp, -8093.633372464128)
self.assertEqual(ws, 3562.30959045887)
self.assertEqual(st, -11822.942962922998)
self.assertEqual(act, 23165)
self.assertEqual(rew, -22999.0)
self.assertEqual(done, 1)
def test_30_run_aggregate_1(self):
def on_step_end(agent, reward, observation, done, action):
if agent.total_step % 1000 == 0:
print('test_30_run_aggregate_1', agent.total_step)
if done:
print('episode terminated at', agent.total_step)
env = gym.make('MountainCar-v0').env
env.seed(self.seed)
agent = rl.Agent(state_space=env.observation_space,
action_space=env.action_space,
discount=0.99,
start_learning_at=0,
memory=None,
q_fun_approx=rl.AggregateApproximator(step_size=0.3,
bins=[64, 64],
init_val=0),
policy=rl.QMaxPolicy(expl_start=False,
nb_rand_steps=0,
e_rand_start=0.1,
e_rand_target=0.1,
e_rand_decay=1 / 10000))
agent.register_callback('on_step_end', on_step_end)
rl.train_agent(env=env, agent=agent, total_steps=30000)
# This is used to test for any numerical discrepancy between runs
fp, ws, st, act, rew, done = agent.get_fingerprint()
print('FINGERPRINT:', fp)
print(' wegight sum:', ws)
print(' st, act, rew, done:', st, act, rew, done)
self.assertEqual(fp, -24059.666698709698)
self.assertEqual(ws, -8850.374069905585)
self.assertEqual(st, -15178.292628804113)
self.assertEqual(act, 29967)
self.assertEqual(rew, -29999.0)
self.assertEqual(done, 1)
def test_20_run_tile_1(self):
def on_step_end(agent, reward, observation, done, action):
if agent.total_step % 1000 == 0:
print('test_20_run_tile_1', agent.total_step)
if done:
print('episode terminated at', agent.total_step)
env = gym.make('MountainCar-v0').env
env.seed(self.seed)
agent = rl.Agent(state_space=env.observation_space,
action_space=env.action_space,
discount=0.99,
start_learning_at=0,
memory=None,
q_fun_approx=rl.TilesApproximator(step_size=0.3,
num_tillings=8,
init_val=0),
policy=rl.QMaxPolicy(expl_start=False,
nb_rand_steps=0,
e_rand_start=1.0,
e_rand_target=0.1,
e_rand_decay=1 / 10000))
agent.register_callback('on_step_end', on_step_end)
rl.train_agent(env=env, agent=agent, total_steps=5000)
# This is used to test for any numerical discrepancy between runs
fp, ws, st, act, rew, done = agent.get_fingerprint()
print('FINGERPRINT:', fp)
print(' wegight sum:', ws)
print(' st, act, rew, done:', st, act, rew, done)
self.assertEqual(fp, -3667.665666738285)
self.assertEqual(ws, -1297.1708778794816)
self.assertEqual(st, -2430.494788858803)
self.assertEqual(act, 5058)
self.assertEqual(rew, -4999.0)
self.assertEqual(done, 1)
def main(self):
args = rl.util.parse_common_args()
rl.util.try_freeze_random_seeds(args.seed, args.reproducible)
#
# Environment
#
# Environment outputs 3-tuple: cos(ang), sin(ang), angular-velocity
# we translate that to 2-tuple: angle [-pi, pi], ang-vel [-8.0, 8.0]
# so we can plot 2d action space nicely
#
# Environment expect continous 1-tuple action representing torque
# in range [-2.0, 2.0], but our agent outputs categorical action 0-4
# so we need to tranlate that to torque
# this is becouse continous actions are not implemented yet
def obs_trans(obs):
"""Translate from 3d obs space to 2d (for easier plotting)"""
theta = np.arctan2(obs[1], obs[0])
vel = obs[2]
return np.array([theta, vel])
def act_trans(act):
"""Translate from categorical actions to continous"""
torques = [-2.0, -0.5, 0.0, 0.5, 2.0]
return np.array([torques[act]])
self.env = rl.util.EnvTranslator(env=gym.make('Pendulum-v0'),
observation_space=gym.spaces.Box(
low=np.array([-np.pi, -8.0]),
high=np.array([np.pi, 8.0])),
observation_translator=obs_trans,
action_space=gym.spaces.Discrete(5),
action_translator=act_trans,
reward_translator=None)
self.env.seed(args.seed)
#
# Agent
#
agent = rl.AgentQ(state_space=self.env.observation_space,
action_space=self.env.action_space,
discount=0.99,
q_fun_approx=rl.QFunctTiles(step_size=0.3,
num_tillings=16,
init_val=0),
policy=rl.PolicyEpsGreedy(expl_start=False,
nb_rand_steps=0,
e_rand_start=0.0,
e_rand_target=0.0,
e_rand_decay=1 / 10000))
#
# Plotting
#
# Need to re-think how plotting works
if args.plot:
fig1 = plt.figure()
self.plotter = rl.util.Plotter(
realtime_plotting=True,
plot_every=1000,
disp_len=1000,
nb_actions=self.env.action_space.n,
figures=(fig1, ),
ax_qmax_wf=fig1.add_subplot(2, 4, 1, projection='3d'),
ax_qmax_im=fig1.add_subplot(2, 4, 2),
ax_policy=fig1.add_subplot(2, 4, 3),
ax_trajectory=fig1.add_subplot(2, 4, 4),
ax_stats=None,
ax_memory=None,
ax_q_series=None,
ax_reward=fig1.add_subplot(2, 1, 2),
)
self.plotter.set_state_action_spaces(
self.env.observation_space.low,
self.env.observation_space.high,
h_line=0.0,
v_line=0.0)
#
# Logging
#
if args.logfile is not None or args.plot:
self.logger = rl.util.Logger()
self.logger.agent = rl.util.Log('Agent')
self.logger.q_val = rl.util.Log('Q_Val')
self.logger.env = rl.util.Log('Environment')
self.logger.hist = rl.util.Log('History', 'All sates visited')
self.logger.memory = rl.util.Log('Memory', 'Full memory dump')
self.logger.approx = rl.util.Log('Approx', 'Approximator')
self.logger.epsumm = rl.util.Log('Episodes')
agent.log_episodes = self.logger.epsumm
agent.log_hist = self.logger.hist
agent.Q.install_logger(self.logger.q_val,
log_every=1000,
samples=(64, 64))
#
# Callback
#
agent.register_callback('on_step_end', self.on_step_end)
#
# Runner
#
try:
rl.train_agent(env=self.env,
agent=agent,
total_steps=1000000,
target_avg_reward=-200)
finally:
if args.logfile is not None:
logger.save(args.logfile)
print('Log saved')
if self.plotter is not None:
plt.show()
def main(self):
args = rl.util.parse_common_args()
rl.util.try_freeze_random_seeds(args.seed, args.reproducible)
#
# Environment
#
# .env at the end removes time limit, see:
# https://stackoverflow.com/questions/42787924/
# why-is-episode-done-after-200-time-steps-gym-environment-mountaincar
self.env = gym.make('MountainCar-v0').env
self.env.seed(args.seed)
test_dqn = False
if test_dqn:
#
# Model
#
q_model = tf.keras.models.Sequential()
q_model.add(tf.keras.layers.Dense(256, 'relu', input_dim=2))
q_model.add(tf.keras.layers.Dense(256, 'relu'))
q_model.add(tf.keras.layers.Dense(3, 'linear'))
q_model.compile(loss='mse',
optimizer=tf.keras.optimizers.RMSprop(lr=0.00025))
#
# Agent - DQN with memory
#
agent = rl.AgentDQN(state_space=self.env.observation_space,
action_space=self.env.action_space,
discount=0.99,
start_learning_at=100000,
memory=rl.MemoryDQN(max_len=100000,
batch_size=1024,
enable_pmr=False,
initial_pmr_error=1000.0),
q_fun_approx=rl.QFunctKeras(q_model),
policy=rl.PolicyEpsGreedy(expl_start=False,
nb_rand_steps=100000,
e_rand_start=1.0,
e_rand_target=0.1,
e_rand_decay=1 /
10000))
else:
#
# Agent - tiles or aggregate
#
agent = rl.AgentQ(
state_space=self.env.observation_space,
action_space=self.env.action_space,
discount=0.99,
q_fun_approx=rl.QFunctTiles(step_size=0.3,
num_tillings=8,
init_val=0),
# q_fun_approx=rl.AggregateApproximator(
# step_size=0.3,
# bins=[64, 64],
# init_val=0),
policy=rl.PolicyEpsGreedy(expl_start=False,
nb_rand_steps=0,
e_rand_start=1.0,
e_rand_target=0.1,
e_rand_decay=1 / 10000))
#
# Plotting
#
# Need to re-think how plotting works
if args.plot:
fig1 = plt.figure()
#fig2 = plt.figure()
self.plotter = rl.util.Plotter(
realtime_plotting=True,
plot_every=1000,
disp_len=1000,
nb_actions=self.env.action_space.n,
figures=(fig1, ),
ax_qmax_wf=fig1.add_subplot(2, 4, 1, projection='3d'),
ax_qmax_im=fig1.add_subplot(2, 4, 2),
ax_policy=fig1.add_subplot(2, 4, 3),
ax_trajectory=fig1.add_subplot(2, 4, 4),
ax_stats=None,
ax_memory=None, #fig2.add_subplot(1,1,1),
ax_q_series=None,
ax_reward=fig1.add_subplot(2, 1, 2),
)
self.plotter.set_state_action_spaces(
self.env.observation_space.low,
self.env.observation_space.high,
h_line=0.0,
v_line=-0.5)
#
# Logging
#
if args.logfile is not None or args.plot:
self.logger = rl.util.Logger()
self.logger.agent = rl.util.Log('Agent')
self.logger.q_val = rl.util.Log('Q_Val')
self.logger.env = rl.util.Log('Environment')
self.logger.hist = rl.util.Log('History', 'All sates visited')
self.logger.memory = rl.util.Log('Memory', 'Full memory dump')
self.logger.approx = rl.util.Log('Approx', 'Approximator')
self.logger.epsumm = rl.util.Log('Episodes')
agent.log_episodes = self.logger.epsumm
agent.log_hist = self.logger.hist
if isinstance(agent, rl.AgentDQN):
agent.memory.install_logger(self.logger.memory, log_every=1000)
agent.Q.install_logger(self.logger.q_val,
log_every=1000,
samples=(64, 64))
agent.register_callback('on_step_end', self.on_step_end)
#
# Runner
#
try:
rl.train_agent(env=self.env,
agent=agent,
total_steps=1000000,
target_avg_reward=-200)
finally:
if args.logfile is not None:
logger.save(args.logfile)
print('Log saved')
if self.plotter is not None:
plt.show()
