Python gym.undo_logger_setup() Examples

The following are 5 code examples of gym.undo_logger_setup(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module gym , or try the search function .
Example #1
Source File: es.py    From learning2run with MIT License 6 votes vote down vote up 
def setup(exp, single_threaded):
    import gym
    gym.undo_logger_setup()
    from . import policies, tf_util

    config = Config(**exp['config'])
    if 'env_id' in exp:
       env = gym.make(exp['env_id'])
    elif 'env_target' in exp:
       env_target = exp['env_target']
       (module_str, cls_str) = env_target.split(":")
       module = importlib.import_module(module_str)
       cls = getattr(module, cls_str)
       env = cls(**exp['env_params'])
    else:
       raise NotImplementedError
    sess = make_session(single_threaded=single_threaded)
    policy = getattr(policies, exp['policy']['type'])(env.observation_space, env.action_space, **exp['policy']['args'])
    tf_util.initialize()

    return config, env, sess, policy
Example #2
Source File: test_envs.py    From universe with MIT License 6 votes vote down vote up 
def test_smoke(env_id):
    """Check that environments start up without errors and that we can extract rewards and observations"""
    gym.undo_logger_setup()
    logging.getLogger().setLevel(logging.INFO)

    env = gym.make(env_id)
    if env.metadata.get('configure.required', False):
        if os.environ.get('FORCE_LATEST_UNIVERSE_DOCKER_RUNTIMES'):  # Used to test universe-envs in CI
            configure_with_latest_docker_runtime_tag(env)
        else:
            env.configure(remotes=1)

    env = wrappers.Unvectorize(env)

    env.reset()
    _rollout(env, timestep_limit=60*30) # Check a rollout
Example #3
Source File: es.py    From evolution-strategies-starter with MIT License 5 votes vote down vote up 
def setup(exp, single_threaded):
    import gym
    gym.undo_logger_setup()
    from . import policies, tf_util

    config = Config(**exp['config'])
    env = gym.make(exp['env_id'])
    sess = make_session(single_threaded=single_threaded)
    policy = getattr(policies, exp['policy']['type'])(env.observation_space, env.action_space, **exp['policy']['args'])
    tf_util.initialize()

    return config, env, sess, policy
Example #4
Source File: test_core_envs_semantics.py    From universe with MIT License 5 votes vote down vote up 
def test_nice_vnc_semantics_match(spec, matcher, wrapper):
    # Check that when running over VNC or using the raw environment,
    # semantics match exactly.
    gym.undo_logger_setup()
    logging.getLogger().setLevel(logging.INFO)

    spaces.seed(0)

    vnc_env = spec.make()
    if vnc_env.metadata.get('configure.required', False):
        vnc_env.configure(remotes=1)
    vnc_env = wrapper(vnc_env)
    vnc_env = wrappers.Unvectorize(vnc_env)

    env = gym.make(spec._kwargs['gym_core_id'])

    env.seed(0)
    vnc_env.seed(0)

    # Check that reset observations work
    reset(matcher, env, vnc_env, stage='initial reset')

    # Check a full rollout
    rollout(matcher, env, vnc_env, timestep_limit=50, stage='50 steps')

    # Reset to start a new episode
    reset(matcher, env, vnc_env, stage='reset to new episode')

    # Check that a step into the next episode works
    rollout(matcher, env, vnc_env, timestep_limit=1, stage='1 step in new episode')

    # Make sure env can be reseeded
    env.seed(1)
    vnc_env.seed(1)
    reset(matcher, env, vnc_env, 'reseeded reset')
    rollout(matcher, env, vnc_env, timestep_limit=1, stage='reseeded step')
Example #5
Source File: utils.py    From categorical-dqn with MIT License 5 votes vote down vote up 
def env_factory(cmdl, mode):
    # Undo the default logger and configure a new one.
    gym.undo_logger_setup()
    logger = logging.getLogger()
    logger.setLevel(logging.WARNING)

    print(clr("[Main] Constructing %s environment." % mode, attrs=['bold']))
    env = gym.make(cmdl.env_name)

    if hasattr(cmdl, 'rescale_dims'):
        state_dims = (cmdl.rescale_dims, cmdl.rescale_dims)
    else:
        state_dims = env.observation_space.shape[0:2]

    env_class, hist_len, cuda = cmdl.env_class, cmdl.hist_len, cmdl.cuda

    if mode == "training":
        env = PreprocessFrames(env, env_class, hist_len, state_dims, cuda)
        if hasattr(cmdl, 'reward_clamp') and cmdl.reward_clamp:
            env = SqueezeRewards(env)
        if hasattr(cmdl, 'done_after_lost_life') and cmdl.done_after_lost_life:
            env = DoneAfterLostLife(env)
        print('-' * 50)
        return env

    elif mode == "evaluation":
        if cmdl.eval_env_name != cmdl.env_name:
            print(clr("[%s] Warning! evaluating on a different env: %s"
                      % ("Main", cmdl.eval_env_name), 'red', attrs=['bold']))
            env = gym.make(cmdl.eval_env_name)

        env = PreprocessFrames(env, env_class, hist_len, state_dims, cuda)
        env = EvaluationMonitor(env, cmdl)
        print('-' * 50)
        return env