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))