def get_dqn_agent(env,
dqn_agent_subtype,
folder='/SRC/pathway/alphaxos/models/',
load=False,
load_mem=True,
side_normalization_factor=1.0):
agent_info = dqn_agents[dqn_agent_subtype]
model = agent_info['qfn'](
out_width=env.action_space.n,
side_normalization_factor=side_normalization_factor)
# see https://github.com/keras-rl/keras-rl/blob/master/examples/duel_dqn_cartpole.py
memory = SequentialMemory(limit=100000, window_length=1)
'''
test_policy = ValidGreedyQPolicy()
test_policy = ValidGreedyQPolicy()
test_policy.env=env
policy = ValidEpsGreedyQPolicy(0.1)
policy.env=env
'''
policy = EpsGreedyQPolicy(regime_params['epsilon-train'])
#policy = ValidEpsGreedyQPolicy(0.1)
policy.env = env
test_policy = None
dqn = DQNAgent(
model=model,
batch_size=regime_params['memory_batch_size'],
gamma=regime_params['gamma'],
nb_actions=env.action_space.n,
memory=memory,
nb_steps_warmup=regime_params['steps_warmup'],
target_model_update=regime_params['steps_target_model_update'],
policy=policy,
test_policy=test_policy,
enable_double_dqn=True)
dqn.compile(Adam(lr=regime_params['learning_rate']), metrics=['mae'])
dqn.modelfile = folder + agent_info['modelfile']
dqn.memoryfile = folder + agent_info['memoryfile']
if load:
dqn.reload()
if load_mem:
dqn.reload_memory()
#dqn.env=env
return dqn
