# Finally, evaluate our algorithm for 5 episodes.
dqn.test(env, nb_episodes=5, visualize=False)
else:
# SARSA
# SARSA does not require a memory.
policy = BoltzmannQPolicy()
model = Sequential()
model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
model.add(Dense(state_size/2))
model.add(Activation('relu'))
model.add(Dense(state_size/4))
model.add(Activation('relu'))
model.add(Dense(state_size/8))
model.add(Activation('relu'))
model.add(Dense(nb_actions, activation='linear'))
print(model.summary())
sarsa = SarsaAgent(model=model, nb_actions=nb_actions, nb_steps_warmup=10, policy=policy)
sarsa.compile(Adam(lr=1e-3), metrics=['mae'])
sarsa.fit(env, nb_steps=500000, visualize=False, verbose=1)
# After training is done, we save the final weights.
sarsa.save_weights('weights.h5f', overwrite=True)
# Finally, evaluate our algorithm for 5 episodes.
sarsa.test(env, nb_episodes=5, visualize=False)
model = Sequential()
model.add(Flatten(input_shape=(1, ) + env.observation_space.shape))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
print(model.summary())
# Sarsa.py in rl.agent update every experience on policy, memory is not used.
policy = BoltzmannQPolicy()
sarsa = SarsaAgent(model=model,
nb_actions=nb_actions,
nb_steps_warmup=10,
policy=policy)
sarsa.compile(Adam(lr=1e-3), metrics=['mae'])
# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
sarsa.fit(env, nb_steps=5000, visualize=False, verbose=2)
# After training is done, we save the final weights.
sarsa.save_weights('sarsa_{}_weights.h5f'.format(ENV_NAME), overwrite=True)
# Finally, evaluate our algorithm for 5 episodes.
sarsa.test(env, nb_episodes=5, visualize=False)