def get_random_policy(env_name, tabular_obs):
if env_name == 'taxi':
env = taxi.Taxi(tabular_obs=tabular_obs)
policy_fn, policy_info_spec = taxi.get_taxi_policy(env,
env,
alpha=0.0,
py=False)
elif env_name == 'grid':
env = navigation.GridWalk(tabular_obs=tabular_obs)
policy_fn, policy_info_spec = navigation.get_navigation_policy(
env, epsilon_explore=1.0, py=False)
elif env_name == 'tree':
env = tree.Tree(branching=2, depth=10)
policy_fn, policy_info_spec = tree.get_tree_policy(env,
epsilon_explore=1.0,
py=False)
else:
raise ValueError('Unknown environment: %s.' % env_name)
return policy_fn, policy_info_spec
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
tf_policy = common_utils.TFAgentsWrappedPolicy(tf_env.time_step_spec(),
tf_env.action_spec(),
policy_fn,
policy_info_spec,
emit_log_probability=True)
return tf_policy, policy_info_spec
def get_dataset(tabular_obs, epsilon_explore, limit):
"""Get on-policy dataset."""
env = navigation.GridWalk(tabular_obs=tabular_obs)
policy_fn, policy_info_spec = navigation.get_navigation_policy(
env, epsilon_explore=0.5)
dataset = gym_onpolicy_dataset.GymOnpolicyDataset(env,
policy_fn,
policy_info_spec,
episode_step_limit=limit)
return dataset
def get_env_and_policy(load_dir,
env_name,
alpha,
env_seed=0,
tabular_obs=False):
if env_name == 'taxi':
env = taxi.Taxi(tabular_obs=tabular_obs)
env.seed(env_seed)
policy_fn, policy_info_spec = taxi.get_taxi_policy(load_dir,
env,
alpha=alpha,
py=False)
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
policy = common_lib.TFAgentsWrappedPolicy(tf_env.time_step_spec(),
tf_env.action_spec(),
policy_fn,
policy_info_spec,
emit_log_probability=True)
elif env_name == 'grid':
env = navigation.GridWalk(tabular_obs=tabular_obs)
env.seed(env_seed)
policy_fn, policy_info_spec = navigation.get_navigation_policy(
env, epsilon_explore=0.1 + 0.6 * (1 - alpha), py=False)
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
policy = common_lib.TFAgentsWrappedPolicy(tf_env.time_step_spec(),
tf_env.action_spec(),
policy_fn,
policy_info_spec,
emit_log_probability=True)
elif env_name == 'low_rank':
env = low_rank.LowRank()
env.seed(env_seed)
policy_fn, policy_info_spec = low_rank.get_low_rank_policy(
env, epsilon_explore=0.1 + 0.8 * (1 - alpha), py=False)
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
policy = common_lib.TFAgentsWrappedPolicy(tf_env.time_step_spec(),
tf_env.action_spec(),
policy_fn,
policy_info_spec,
emit_log_probability=True)
elif env_name == 'tree':
env = tree.Tree(branching=2, depth=10)
env.seed(env_seed)
policy_fn, policy_info_spec = tree.get_tree_policy(
env, epsilon_explore=0.1 + 0.8 * (1 - alpha), py=False)
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
policy = common_lib.TFAgentsWrappedPolicy(tf_env.time_step_spec(),
tf_env.action_spec(),
policy_fn,
policy_info_spec,
emit_log_probability=True)
elif env_name == 'lowrank_tree':
env = tree.Tree(branching=2, depth=3, duplicate=10)
env.seed(env_seed)
policy_fn, policy_info_spec = tree.get_tree_policy(
env, epsilon_explore=0.1 + 0.8 * (1 - alpha), py=False)
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
policy = common_lib.TFAgentsWrappedPolicy(tf_env.time_step_spec(),
tf_env.action_spec(),
policy_fn,
policy_info_spec,
emit_log_probability=True)
elif env_name.startswith('bandit'):
num_arms = int(env_name[6:]) if len(env_name) > 6 else 2
env = bandit.Bandit(num_arms=num_arms)
env.seed(env_seed)
policy_fn, policy_info_spec = bandit.get_bandit_policy(
env, epsilon_explore=1 - alpha, py=False)
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
policy = common_lib.TFAgentsWrappedPolicy(tf_env.time_step_spec(),
tf_env.action_spec(),
policy_fn,
policy_info_spec,
emit_log_probability=True)
elif env_name == 'small_tree':
env = tree.Tree(branching=2, depth=3, loop=True)
env.seed(env_seed)
policy_fn, policy_info_spec = tree.get_tree_policy(
env, epsilon_explore=0.1 + 0.8 * (1 - alpha), py=False)
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
policy = common_lib.TFAgentsWrappedPolicy(tf_env.time_step_spec(),
tf_env.action_spec(),
policy_fn,
policy_info_spec,
emit_log_probability=True)
elif env_name == 'CartPole-v0':
tf_env, policy = get_env_and_dqn_policy(
env_name,
os.path.join(load_dir, 'CartPole-v0', 'train', 'policy'),
env_seed=env_seed,
epsilon=0.3 + 0.15 * (1 - alpha))
elif env_name == 'cartpole': # Infinite-horizon cartpole.
tf_env, policy = get_env_and_dqn_policy(
'CartPole-v0',
os.path.join(load_dir, 'CartPole-v0-250', 'train', 'policy'),
env_seed=env_seed,
epsilon=0.3 + 0.15 * (1 - alpha))
env = InfiniteCartPole()
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
elif env_name == 'FrozenLake-v0':
tf_env, policy = get_env_and_dqn_policy('FrozenLake-v0',
os.path.join(
load_dir, 'FrozenLake-v0',
'train', 'policy'),
env_seed=env_seed,
epsilon=0.2 * (1 - alpha),
ckpt_file='ckpt-100000')
elif env_name == 'frozenlake': # Infinite-horizon frozenlake.
tf_env, policy = get_env_and_dqn_policy('FrozenLake-v0',
os.path.join(
load_dir, 'FrozenLake-v0',
'train', 'policy'),
env_seed=env_seed,
epsilon=0.2 * (1 - alpha),
ckpt_file='ckpt-100000')
env = InfiniteFrozenLake()
tf_env = tf_py_environment.TFPyEnvironment(gym_wrapper.GymWrapper(env))
elif env_name in ['Reacher-v2', 'reacher']:
if env_name == 'Reacher-v2':
env = suites.load_mujoco(env_name)
else:
env = gym_wrapper.GymWrapper(InfiniteReacher())
env.seed(env_seed)
tf_env = tf_py_environment.TFPyEnvironment(env)
sac_policy = get_sac_policy(tf_env)
directory = os.path.join(load_dir, 'Reacher-v2', 'train', 'policy')
policy = load_policy(sac_policy, env_name, directory)
policy = GaussianPolicy(policy,
0.4 - 0.3 * alpha,
emit_log_probability=True)
elif env_name == 'HalfCheetah-v2':
env = suites.load_mujoco(env_name)
env.seed(env_seed)
tf_env = tf_py_environment.TFPyEnvironment(env)
sac_policy = get_sac_policy(tf_env)
directory = os.path.join(load_dir, env_name, 'train', 'policy')
policy = load_policy(sac_policy, env_name, directory)
policy = GaussianPolicy(policy,
0.2 - 0.1 * alpha,
emit_log_probability=True)
else:
raise ValueError('Unrecognized environment %s.' % env_name)
return tf_env, policy