}, {
"key": "repeat_action_probability",
"value": 0.
}])
# --- Instantiate qnetwork ---
qnetwork = MyQNetwork(env, parameters.rms_decay, parameters.rms_epsilon,
parameters.momentum, parameters.clip_delta,
parameters.freeze_interval, parameters.batch_size,
parameters.network_type, parameters.update_rule,
parameters.batch_accumulator, rng)
# --- Instantiate agent ---
agent = ALEAgent(
env, qnetwork, parameters.replay_memory_size,
max(env.inputDimensions()[i][0]
for i in range(len(env.inputDimensions()))), parameters.batch_size,
rng)
# --- Create unique filename for FindBestController ---
h = hash(vars(parameters), hash_name="sha1")
fname = "ALE_" + h
print("The parameters hash is: {}".format(h))
print("The parameters are: {}".format(parameters))
# --- Bind controllers to the agent ---
# Before every training epoch (periodicity=1), we want to print a summary of the agent's epsilon, discount and
# learning rate as well as the training epoch number.
agent.attach(bc.VerboseController(evaluateOn='epoch', periodicity=1))
# During training epochs, we want to train the agent after every [parameters.update_frequency] action it takes.
"value": 0.
}])
# --- Instantiate qnetwork ---
qnetwork = MyQNetwork(env, parameters.rms_decay, parameters.rms_epsilon,
parameters.momentum, parameters.clip_delta,
parameters.freeze_interval, parameters.batch_size,
parameters.update_rule, rng)
test_policy = EpsilonGreedyPolicy(qnetwork, env.nActions(), rng, 0.05)
# --- Instantiate agent ---
agent = NeuralAgent(env,
qnetwork,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0]
for i in range(len(env.inputDimensions()))),
parameters.batch_size,
rng,
test_policy=test_policy)
# --- Create unique filename for FindBestController ---
h = hash(vars(parameters), hash_name="sha1")
fname = "ALE_" + h
print("The parameters hash is: {}".format(h))
print("The parameters are: {}".format(parameters))
# --- Bind controllers to the agent ---
# Before every training epoch (periodicity=1), we want to print a summary of the agent's epsilon, discount and
# learning rate as well as the training epoch number.
agent.attach(bc.VerboseController(evaluate_on='epoch', periodicity=1))
parameters.momentum,
parameters.clip_delta,
parameters.freeze_interval,
parameters.batch_size,
parameters.update_rule,
rng,
)
test_policy = EpsilonGreedyPolicy(qnetwork, env.nActions(), rng, 0.05)
# --- Instantiate agent ---
agent = NeuralAgent(
env,
qnetwork,
parameters.replay_memory_size,
max(env.inputDimensions()[i][0] for i in range(len(env.inputDimensions()))),
parameters.batch_size,
rng,
test_policy=test_policy,
)
# --- Create unique filename for FindBestController ---
h = hash(vars(parameters), hash_name="sha1")
fname = "ALE_" + h
print("The parameters hash is: {}".format(h))
print("The parameters are: {}".format(parameters))
# --- Bind controllers to the agent ---
# Before every training epoch (periodicity=1), we want to print a summary of the agent's epsilon, discount and
# learning rate as well as the training epoch number.
agent.attach(bc.VerboseController(evaluate_on="epoch", periodicity=1))
