def test_act(self):
env = OpenAIGymEnv("Pong-v0", frameskip=4, max_num_noops=30, episodic_life=True)
agent_config = config_from_path("configs/ray_apex_for_pong.json")
if get_backend() == "pytorch":
agent_config["memory_spec"]["type"] = "mem_prioritized_replay"
agent = DQNAgent.from_spec(
# Uses 2015 DQN parameters as closely as possible.
agent_config,
state_space=env.state_space,
# Try with "reduced" action space (actually only 3 actions, up, down, no-op)
action_space=env.action_space
)
state = env.reset()
action = agent.get_action(state)
print("Component call count = {}".format(Component.call_count))
state_space = env.state_space
count = 200
samples = state_space.sample(count)
start = time.perf_counter()
for s in samples:
action = agent.get_action(s)
end = time.perf_counter() - start
print("Took {} s for {} separate actions, mean = {}".format(end, count, end / count))
# Now instead test 100 batch actions
samples = state_space.sample(count)
start = time.perf_counter()
action = agent.get_action(samples)
end = time.perf_counter() - start
print("Took {} s for {} batched actions.".format(end, count))
profile = Component.call_times
print_call_chain(profile, False, 0.03)
def test_readme_example(self):
"""
Tests deterministic functionality of RandomEnv.
"""
from rlgraph.agents import DQNAgent
from rlgraph.environments import OpenAIGymEnv
environment = OpenAIGymEnv('CartPole-v0')
config = config_from_path("../../examples/configs/dqn_cartpole.json")
# Create from .json file or dict, see agent API for all
# possible configuration parameters.
agent = DQNAgent.from_spec(config,
state_space=environment.state_space,
action_space=environment.action_space)
# Get an action, take a step, observe reward.
state = environment.reset()
preprocessed_state, action = agent.get_action(
states=state, extra_returns="preprocessed_states")
# Execute step in environment.
next_state, reward, terminal, info = environment.step(action)
# Observe result.
agent.observe(preprocessed_states=preprocessed_state,
actions=action,
internals=[],
next_states=next_state,
rewards=reward,
terminals=terminal)
# Call update when desired:
loss = agent.update()
def test_openai_atari_env(self):
env = OpenAIGymEnv("Pong-v0")
# Simple test runs with fixed actions.
s = env.reset()
# Assert we have pixels.
self.assertGreaterEqual(np.mean(s), 0)
self.assertLessEqual(np.mean(s), 255)
accum_reward = 0.0
for _ in range(100):
s, r, t, _ = env.step(env.action_space.sample())
assert isinstance(r, np.ndarray)
assert r.dtype == np.float32
assert isinstance(t, bool)
self.assertGreaterEqual(np.mean(s), 0)
self.assertLessEqual(np.mean(s), 255)
accum_reward += r
print("Accumulated Reward: ".format(accum_reward))