parameters = process_args(sys.argv[1:], Defaults)
if parameters.deterministic:
rng = np.random.RandomState(123456)
else:
rng = np.random.RandomState()
# --- Instantiate environment ---
env = maze_env(rng, higher_dim_obs=HIGHER_DIM_OBS)
# --- Instantiate learning_algo ---
learning_algo = CRAR(env,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_norm,
parameters.freeze_interval,
parameters.batch_size,
parameters.update_rule,
rng,
double_Q=True,
high_int_dim=HIGH_INT_DIM,
internal_dim=3)
train_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, 1.)
test_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, 0.1)
# --- Instantiate agent ---
agent = NeuralAgent(env,
learning_algo,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0]
for i in range(len(env.inputDimensions()))),
rng = np.random.RandomState(parameters.seed)
print(" deterministic, seed: ",parameters.seed)
input_nnet = "normal_seed" + str(parameters.seed) + "_lr" + str(parameters.learning_rate) + "_lrd" + str(parameters.learning_rate_decay)
else:
rng = np.random.RandomState()
if parameters.dumpname != "":
input_nnet = parameters.dumpname
print("input nnet= ", input_nnet)
# --- Instantiate environment ---
env = maze_env(rng, higher_dim_obs=parameters.high_dim_obs, show_game=False)
# --- Instantiate learning_algo ---
learning_algo = CRAR(
env,
rng,
double_Q=True,
high_int_dim=HIGH_INT_DIM,
internal_dim=3,
div_entrop_loss=1.)
train_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, 1.)
test_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, 0.1)
# --- Instantiate agent ---
agent = NeuralAgent(
env,
learning_algo,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0] for i in range(len(env.inputDimensions()))),
parameters.batch_size,
rng,
parameters = process_args(sys.argv[1:], Defaults)
if parameters.deterministic:
rng = np.random.RandomState(123456)
else:
rng = np.random.RandomState()
# --- Instantiate environment ---
env = simple_maze_env(rng, higher_dim_obs=HIGHER_DIM_OBS)
# --- Instantiate learning_algo ---
learning_algo = CRAR(
env,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_norm,
parameters.freeze_interval,
parameters.batch_size,
parameters.update_rule,
rng,
high_int_dim=False,
internal_dim=2)
test_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng, 1.)
# --- Instantiate agent ---
agent = NeuralAgent(
env,
learning_algo,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0] for i in range(len(env.inputDimensions()))),
parameters.batch_size,
parameters = process_args(sys.argv[1:], Defaults)
if parameters.deterministic:
rng = np.random.RandomState(parameters.seed)
else:
rng = np.random.RandomState()
# --- Instantiate environment ---
env = catcher_env(rng,
higher_dim_obs=HIGHER_DIM_OBS,
reverse=False,
show_game=True)
# --- Instantiate learning algorithm ---
learning_algo = CRAR(env,
rng,
double_Q=True,
high_int_dim=HIGH_INT_DIM,
internal_dim=3)
test_policy = EpsilonGreedyPolicy(learning_algo, env.nActions(), rng,
0.1) #1.)
# --- Instantiate agent ---
agent = NeuralAgent(env,
learning_algo,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0]
for i in range(len(env.inputDimensions()))),
parameters.batch_size,
rng,
test_policy=test_policy)
