def crossover_agents(p1, p2):
p1_weights = p1.get_flattened_weights_of_model()
p2_weights = p2.get_flattened_weights_of_model()
c1_weights, c2_weights = u.numpy_array_crossover(p1_weights, p2_weights)
c1 = agents.NeuralAgent()
c1.set_weights_of_model(c1_weights)
c2 = agents.NeuralAgent()
c2.set_weights_of_model(c2_weights)
return c1, c2
type=str,
default=None,
help="weights files, only valid for --agent=neural")
parser.add_argument(
'--lite-weights',
type=str,
default=None,
help="tf lite weights files, must be set for --agent=neural_lite")
parser.add_argument('--trials',
type=int,
default=10,
help='num trials to run; new agent per trial')
opts = parser.parse_args()
evaluator = cartpole_fitness.CartPoleFitness(render=opts.env_render)
print("trial\ttotal_reward")
for trial_idx in range(opts.trials):
if opts.agent == 'random':
agent = agents.RandomAgent()
elif opts.agent == 'neural':
agent = agents.NeuralAgent()
if opts.weights is not None:
agent.set_weights_of_model(np.load(opts.weights))
elif opts.agent == 'neural_lite':
agent = agents.NeuralLiteAgent(tflite_file=opts.lite_weights)
else:
raise Exception("unexpected agent type [%s]" % opts.agent)
print("%d\t%d" % (trial_idx, evaluator.fitness(agent)))
sys.stdout.flush()
def new_member_bytes():
random_agent = agents.NeuralAgent()
flat_buffer_bytes = convert_to_tflite.convert_to_file_bytes(random_agent)
return flat_buffer_bytes
plt.ylabel("Cumulative Reward")
plt.show()
def plotQ(Q):
states = [[0, 0], [0, 1], [1, 0], [1, 1]]
for state in states:
for a in [0, 1]:
print("Q[{},{}]={}".format(state, a, Q[env.asint(state), a]))
# Number of iterations
n_iter = 1000
# environment specs
env = EvidenceEnv(n=2, p=0.75)
agent = agents.RandomAgent(env)
runAgent()
# define agent
agent = agents.TabularQAgent(env)
plotQ(agent.Q)
runAgent()
plotQ(agent.Q)
actualQ = agent.Q
agent = agents.NeuralAgent(env, actualQ)
plotQ(agent.Q)
runAgent()
plotQ(agent.Q)
def new_agent():
return agents.NeuralAgent()