def test_dqn_agent(self):
config = {
'seed': 10,
'batch_size': 16,
'state_shape': (2, ),
'actions': 2,
'action_shape': (),
'update_rate': 1,
'update_repeat': 4,
'min_replay_size': 50,
'memory_capacity': 50,
"exploration": "epsilon_decay",
"exploration_param": {
"epsilon": 1,
"epsilon_final": 0,
"epsilon_states": 50
},
'target_network_update_rate': 1.0,
'use_target_network': True,
"alpha": 0.0005,
"gamma": 0.99,
"tau": 1.0
}
tf.reset_default_graph()
tf.set_random_seed(10)
config = create_config(config)
network_builder = NeuralNetwork.layered_network(
layers=[{
'type': 'dense',
'num_outputs': 16
}, {
'type': 'linear',
'num_outputs': 2
}])
agent = DQNAgent(config=config, network_builder=network_builder)
state = (1, 0)
rewards = [0.0] * 100
for n in xrange(10000):
action = agent.get_action(state=state)
if action == 0:
state = (1, 0)
reward = 0.0
terminal = False
else:
state = (0, 1)
reward = 1.0
terminal = False
agent.add_observation(state=state,
action=action,
reward=reward,
terminal=terminal)
rewards[n % 100] = reward
if sum(rewards) == 100.0:
return
assert (sum(rewards) == 100.0)
def test_trpo_agent(self):
config = {
'batch_size': 8,
'max_episode_length': 4,
'continuous': False,
'state_shape': (2, ),
'actions': 2,
'action_shape': ()
}
tf.reset_default_graph()
config = create_config(config)
network_builder = NeuralNetwork.layered_network(
layers=[{
'type': 'dense',
'num_outputs': 32
}, {
'type': 'linear',
'num_outputs': 2
}])
agent = DQNAgent(config=config, network_builder=network_builder)
state = (1, 0)
rewards = [0.0] * 100
for n in range(1000):
action = agent.get_action(state=state)
if action == 0:
state = (1, 0)
reward = 0.0
terminal = False
else:
state = (0, 1)
reward = 1.0
terminal = True
agent.add_observation(state=state,
action=action,
reward=reward,
terminal=terminal)
rewards[n % 100] = reward
if sum(rewards) == 100.0:
return
