Python get_reward_fn Examples

Example #1

    def set_reward_from_config(self, env_name: str, env_config: dict):
        """Build and set the reward function from environment configurations.

        Args:
            env_name: the environment's id
            env_config: the environment's configuration
        """
        self.reward_fn = get_reward_fn(env_name, env_config)

Example #2

def env_reward(request, envs, env_config):
    env_name = request.param
    if "HalfCheetah" in env_name:
        env_config["exclude_current_positions_from_observation"] = False
    if "IndustrialBenchmark" in env_name:
        env_config["max_episode_steps"] = 200

    env = envs[env_name](env_config)
    reward_fn = get_reward_fn(env_name, env_config)
    return env, reward_fn

Example #3

File: test_rewards.py Project: rudrasohan/raylab

def env_reward(request, envs, env_config):
    env_name: str = request.param
    if env_name.endswith("-v3"):
        kwargs = dict(exclude_current_positions_from_observation=False)
        env_config["kwargs"] = kwargs
    if "IndustrialBenchmark" in env_name:
        env_config["max_episode_steps"] = 200

    env = envs[env_name](env_config)
    reward_fn = get_reward_fn(env_name, env_config)
    return env, reward_fn

Example #4

def env_reward(request, envs, env_config):
    env_name = request.param
    if any(
        prefix in env_name for prefix in "HalfCheetah Walker2d Swimmer Hopper".split()
    ):
        env_config["exclude_current_positions_from_observation"] = False
    if "IndustrialBenchmark" in env_name:
        env_config["max_episode_steps"] = 200

    env = envs[env_name](env_config)
    reward_fn = get_reward_fn(env_name, env_config)
    return env, reward_fn

Example #5

def test_set_reward_from_config(policy, envs):  # pylint:disable=unused-argument
    obs_space, action_space = policy.observation_space, policy.action_space
    batch_size = 10
    obs = fake_space_samples(obs_space, batch_size=batch_size)
    act = fake_space_samples(action_space, batch_size=batch_size)
    new_obs = fake_space_samples(obs_space, batch_size=batch_size)
    obs, act, new_obs = map(policy.convert_to_tensor, (obs, act, new_obs))

    policy.set_reward_from_config("MockEnv", {})

    original_fn = get_reward_fn("MockEnv", {})
    expected_rew = original_fn(obs, act, new_obs)
    rew = policy.reward_fn(obs, act, new_obs)

    assert torch.allclose(rew, expected_rew)

Example #6

File: envfn.py Project: stjordanis/raylab

 def set_reward_from_config(self):
     """Build and set the reward function from environment configurations."""
     env_id, env_config = self.config["env"], self.config["env_config"]
     self.reward_fn = envs.get_reward_fn(env_id, env_config)
     self._set_reward_hook()

Example #7

File: test_linear_redundant.py Project: stjordanis/raylab

def reward_fn(env_name, env_config, size):
    base = envs.get_reward_fn(env_name, env_config)
    wrapped = LinearRedundant.wrap_env_function(base, size)
    return wrapped

Example #8

def env_reward(request, envs, env_config):
    env_name = request.param
    env = envs[env_name](env_config)
    reward_fn = get_reward_fn(env_name, env_config)
    return env, reward_fn

Example #9

def reward_fn(env_name, env_config, size):
    base = envs.get_reward_fn(env_name, env_config)
    wrapped = CorrelatedIrrelevant.wrap_env_function(base, size)
    return wrapped

Example #10

File: test_random_irrelevant.py Project: stjordanis/raylab

def reward_fn(env_name, size, env_config):
    base = envs.get_reward_fn(env_name, env_config)
    wrapped = RandomIrrelevant.wrap_env_function(base, size)
    return wrapped