def test_constructor(self):
try:
OvercookedEnv.from_mdp(self.base_mdp, horizon=10)
except Exception as e:
self.fail("Failed to instantiate OvercookedEnv:\n{}".format(e))
with self.assertRaises(TypeError):
OvercookedEnv.from_mdp(self.base_mdp, **{"invalid_env_param": None})
def activate(self):
super(OvercookedGame, self).activate()
# Sanity check at start of each game
if not self.npc_players.union(self.human_players) == set(self.players):
raise ValueError("Inconsistent State")
self.curr_layout = self.layouts.pop()
mdp = OvercookedGridworld.from_layout_name(self.curr_layout,
**self.mdp_params)
self.env = OvercookedEnv.from_mdp(mdp)
if self.show_potential:
self.mp = MotionPlanner.from_pickle_or_compute(
self.mdp, counter_goals=NO_COUNTERS_PARAMS)
if self.show_potential:
self.phi = self.mdp.potential_function(self.state,
self.mp,
gamma=0.99)
self.start_time = time()
self.curr_tick = 0
self.score = 0
self.threads = []
for npc_policy in self.npc_policies:
self.npc_policies[npc_policy].reset()
self.npc_state_queues[npc_policy].put(self.state)
t = Thread(target=self.npc_policy_consumer, args=(npc_policy, ))
self.threads.append(t)
t.start()
def __init__(self,
mdp_params,
env_params={},
mdp_fn_params=None,
force_compute=False,
mlp_params=NO_COUNTERS_PARAMS,
debug=False):
"""
mdp_params (dict): params for creation of an OvercookedGridworld instance through the `from_layout_name` method
env_params (dict): params for creation of an OvercookedEnv
mdp_fn_params (dict): params to setup random MDP generation
force_compute (bool): whether should re-compute MediumLevelPlanner although matching file is found
mlp_params (dict): params for MediumLevelPlanner
"""
assert type(mdp_params) is dict, "mdp_params must be a dictionary"
if mdp_fn_params is None:
mdp = OvercookedGridworld.from_layout_name(**mdp_params)
self.mdp_fn = lambda: mdp
self.env = OvercookedEnv.from_mdp(mdp, **env_params)
else:
self.mdp_fn = LayoutGenerator.mdp_gen_fn_from_dict(
mdp_params, **mdp_fn_params)
self.env = OvercookedEnv(self.mdp_fn, **env_params)
self.force_compute = force_compute
self.debug = debug
self.mlp_params = mlp_params
self._mlp = None
def test_scenario_1_s(self):
# Smaller version of the corridor collisions scenario above
# to facilitate DRL training
scenario_1_mdp = OvercookedGridworld.from_layout_name(
'scenario1_s', start_order_list=['any'], cook_time=5)
mlp = MediumLevelPlanner.from_pickle_or_compute(
scenario_1_mdp, NO_COUNTERS_PARAMS, force_compute=force_compute)
a0 = GreedyHumanModel(mlp)
a1 = CoupledPlanningAgent(mlp)
agent_pair = AgentPair(a0, a1)
start_state = OvercookedState(
[P((2, 1), s, Obj('onion', (2, 1))),
P((4, 2), s)], {},
order_list=['onion'])
env = OvercookedEnv.from_mdp(scenario_1_mdp,
start_state_fn=lambda: start_state)
trajectory, time_taken_hr, _, _ = env.run_agents(
agent_pair, include_final_state=True, display=DISPLAY)
env.reset()
print("\n" * 5)
print("-" * 50)
a0 = CoupledPlanningAgent(mlp)
a1 = CoupledPlanningAgent(mlp)
agent_pair = AgentPair(a0, a1)
trajectory, time_taken_rr, _, _ = env.run_agents(
agent_pair, include_final_state=True, display=DISPLAY)
print("H+R time taken: ", time_taken_hr)
print("R+R time taken: ", time_taken_rr)
self.assertGreater(time_taken_hr, time_taken_rr)
def check_single_motion_plan(self,
motion_planner,
start_pos_and_or,
goal_pos_and_or,
expected_length=None):
dummy_agent = P((3, 2), n)
start_state = OvercookedState([P(*start_pos_and_or), dummy_agent], {},
all_orders=simple_mdp.start_all_orders)
action_plan, pos_and_or_plan, plan_cost = motion_planner.get_plan(
start_pos_and_or, goal_pos_and_or)
# Checking that last state obtained matches goal position
self.assertEqual(pos_and_or_plan[-1], goal_pos_and_or)
# In single motion plans the graph cost should be equal to
# the plan cost (= plan length) as agents should never STAY
graph_plan_cost = sum(
[motion_planner._graph_action_cost(a) for a in action_plan])
self.assertEqual(plan_cost, graph_plan_cost)
joint_action_plan = [(a, stay) for a in action_plan]
env = OvercookedEnv.from_mdp(motion_planner.mdp, horizon=1000)
resulting_state, _ = env.execute_plan(start_state, joint_action_plan)
self.assertEqual(resulting_state.players_pos_and_or[0],
goal_pos_and_or)
if expected_length is not None:
self.assertEqual(len(action_plan), expected_length)
def check_joint_plan(self,
joint_motion_planner,
start,
goal,
times=None,
min_t=None,
display=False):
"""Runs the plan in the environment and checks that the intended goals are achieved."""
debug = False
action_plan, end_pos_and_orients, plan_lengths = joint_motion_planner.get_low_level_action_plan(
start, goal)
if debug:
print("Start state: {}, Goal state: {}, Action plan: {}".format(
start, goal, action_plan))
start_state = OvercookedState(
[P(*start[0]), P(*start[1])], {},
all_orders=simple_mdp.start_all_orders)
env = OvercookedEnv.from_mdp(joint_motion_planner.mdp, horizon=1000)
resulting_state, _ = env.execute_plan(start_state,
action_plan,
display=display)
self.assertTrue(
any([
agent_goal in resulting_state.players_pos_and_or
for agent_goal in goal
]))
self.assertEqual(resulting_state.players_pos_and_or,
end_pos_and_orients)
self.assertEqual(len(action_plan), min(plan_lengths))
if min_t is not None: self.assertEqual(len(action_plan), min_t)
if times is not None: self.assertEqual(plan_lengths, times)
def test_slowed_down_agent(self):
def should_stop(step_num, stop_every_n_steps):
# currently SlowDownAgent always stops at 2nd step
return not bool((i-1) % stop_every_n_steps)
horizon = 100
#NOTE: if stop_every_n_steps is 3 this would not work because of rounding error
# (ok for practical purposes, will just skip turn later but would fail test below)
for stop_every_n_steps in [2, 4]:
slowdown_rate = 1 - 1/stop_every_n_steps
agent_pair = AgentPair(
SlowedDownAgent(RandomAgent(), slowdown_rate),
SlowedDownAgent(RandomAgent(), slowdown_rate)
)
skip_action_probs = SlowedDownAgent(RandomAgent()).skip_action[1]["action_probs"].tolist()
env = OvercookedEnv.from_mdp(large_mdp, horizon=horizon)
trajectory, time_taken, _, _ = env.run_agents(agent_pair, include_final_state=True, display=DISPLAY)
for i, traj_step in enumerate(trajectory):
(s_t, a_t, r_t, done, info) = traj_step
if not done:
agent_0_probs = info["agent_infos"][0]["action_probs"]
agent_1_probs = info["agent_infos"][1]["action_probs"]
if should_stop(i, stop_every_n_steps):
self.assertEqual(agent_0_probs.tolist(), skip_action_probs)
self.assertEqual(agent_1_probs.tolist(), skip_action_probs)
else:
self.assertNotEqual(agent_0_probs.tolist(), skip_action_probs)
self.assertNotEqual(agent_1_probs.tolist(), skip_action_probs)
def test_scenario_1(self):
# Myopic corridor collision
#
# X X X X X O X D X X X X X
# X ↓Ho X X
# X X X X X X X X ↓R X
# X X
# X S X X X X X X X X P P X
#
# H on left with onion, further away to the tunnel entrance than R.
# Optimal planner tells R to go first and that H will wait
# for R to pass. H however, starts going through the tunnel
# and they get stuck. The H plan is a bit extreme (it would probably
# realize that it should retrace it's steps at some point)
scenario_1_mdp = OvercookedGridworld.from_layout_name(
'small_corridor', start_order_list=['any'], cook_time=5)
mlp = MediumLevelPlanner.from_pickle_or_compute(
scenario_1_mdp, NO_COUNTERS_PARAMS, force_compute=force_compute)
a0 = GreedyHumanModel(mlp)
a1 = CoupledPlanningAgent(mlp)
agent_pair = AgentPair(a0, a1)
start_state = OvercookedState(
[P((2, 1), s, Obj('onion', (2, 1))),
P((10, 2), s)], {},
order_list=['onion'])
env = OvercookedEnv.from_mdp(scenario_1_mdp,
start_state_fn=lambda: start_state)
env.run_agents(agent_pair, include_final_state=True, display=DISPLAY)
def test_one_player_env(self):
mdp = OvercookedGridworld.from_layout_name("cramped_room_single")
env = OvercookedEnv.from_mdp(mdp, horizon=12)
a0 = FixedPlanAgent([stay, w, w, e, e, n, e, interact, w, n, interact])
ag = AgentGroup(a0)
env.run_agents(ag, display=False)
self.assertEqual(env.state.players_pos_and_or, (((2, 1), (0, -1)), ))
def test_transitions_and_environment(self):
bad_state = OvercookedState([P((0, 0), s), P((3, 1), s)], {},
order_list=[])
with self.assertRaises(AssertionError):
self.base_mdp.get_state_transition(bad_state, stay)
env = OvercookedEnv.from_mdp(self.base_mdp)
env.state.order_list = ['onion', 'any']
def check_transition(action, expected_state, expected_reward=0):
state = env.state
pred_state, sparse_reward, dense_reward, _ = self.base_mdp.get_state_transition(
state, action)
self.assertEqual(
pred_state, expected_state,
'\n' + str(pred_state) + '\n' + str(expected_state))
new_state, sparse_reward, _, _ = env.step(action)
self.assertEqual(new_state, expected_state)
self.assertEqual(sparse_reward, expected_reward)
check_transition([n, e],
OvercookedState([P(
(1, 1), n), P((3, 1), e)], {},
order_list=['onion', 'any']))
def test_get_encoding_function(self):
mdp = OvercookedGridworld.from_layout_name("cramped_room")
mdp_params = mdp.mdp_params
env_params = {"horizon": 100}
env = OvercookedEnv.from_mdp(mdp, **env_params)
state = mdp.get_standard_start_state()
example_encoding_fns_names = ["mdp.multi_hot_orders_encoding", "env.featurize_state_mdp", "env.lossless_state_encoding_mdp"]
example_encoding_fns = [mdp.multi_hot_orders_encoding, env.featurize_state_mdp, env.lossless_state_encoding_mdp]
for encoding_fn_name, encoding_fn in zip(example_encoding_fns_names, example_encoding_fns):
encoding_fn_from_name = get_encoding_function(encoding_fn_name, env=env)
self.assertEqual(encoding_fn_from_name, encoding_fn)
if encoding_fn_name.split(".")[0] == "mdp":
encoding_fn_from_name = get_encoding_function(encoding_fn_name, mdp=mdp)
self.assertEqual(encoding_fn_from_name, encoding_fn)
encoding_fn_from_name = get_encoding_function(encoding_fn_name, mdp_params=mdp_params)
# compare names as new instance of mdp is created
self.assertEqual(encoding_fn_from_name.__name__, encoding_fn.__name__)
else:
encoding_fn_from_name = get_encoding_function(encoding_fn_name, env_params=env_params, mdp_params=mdp_params)
# compare names as new instance of env is created
self.assertEqual(encoding_fn_from_name.__name__, encoding_fn.__name__)
expected_encoded_state_dict = {str(i): fn(state) for i, fn in enumerate(example_encoding_fns)}
actual_encoded_state_dict = get_encoding_function({str(i): fn_name for i, fn_name in enumerate(example_encoding_fns_names)}, env=env)(state)
self.assertEqual(expected_encoded_state_dict.keys(), actual_encoded_state_dict.keys())
for k in expected_encoded_state_dict.keys():
self.assertTrue(np.array_equal(expected_encoded_state_dict[k], actual_encoded_state_dict[k]))
def setUp(self):
self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room")
self.mlam = MediumLevelActionManager.from_pickle_or_compute(
self.base_mdp, NO_COUNTERS_PARAMS, force_compute=True)
self.env = OvercookedEnv.from_mdp(self.base_mdp, **DEFAULT_ENV_PARAMS)
self.rnd_agent_pair = AgentPair(GreedyHumanModel(self.mlam),
GreedyHumanModel(self.mlam))
np.random.seed(0)
def test_one_coupled_one_fixed(self):
a0 = CoupledPlanningAgent(self.mlp_large)
a1 = FixedPlanAgent([s, e, n, w])
agent_pair = AgentPair(a0, a1)
env = OvercookedEnv.from_mdp(large_mdp, horizon=10)
trajectory, time_taken, _, _ = env.run_agents(agent_pair,
include_final_state=True,
display=DISPLAY)
self.assertEqual(time_taken, 10)
def test_starting_obj_randomization(self):
self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room")
start_state_fn = self.base_mdp.get_random_start_state_fn(random_start_pos=False, rnd_obj_prob_thresh=0.8)
env = OvercookedEnv.from_mdp(self.base_mdp, start_state_fn)
start_state = env.state.all_objects_list
for _ in range(3):
env.reset()
curr_terrain = env.state.all_objects_list
self.assertFalse(np.array_equal(start_state, curr_terrain))
def test_fixed_plan_agents(self):
a0 = FixedPlanAgent([s, e, n, w])
a1 = FixedPlanAgent([s, w, n, e])
agent_pair = AgentPair(a0, a1)
env = OvercookedEnv.from_mdp(large_mdp, horizon=10)
trajectory, time_taken, _, _ = env.run_agents(agent_pair, include_final_state=True, display=DISPLAY)
end_state = trajectory[-1][0]
self.assertEqual(time_taken, 10)
self.assertEqual(env.mdp.get_standard_start_state().player_positions, end_state.player_positions)
def get_mdps_and_envs_from_trajectories(trajectories):
mdps, envs = [], []
for idx in range(len(trajectories["ep_lengths"])):
mdp_params = copy.deepcopy(trajectories["mdp_params"][idx])
env_params = copy.deepcopy(trajectories["env_params"][idx])
mdp = OvercookedGridworld.from_layout_name(**mdp_params)
env = OvercookedEnv.from_mdp(mdp, **env_params)
mdps.append(mdp)
envs.append(env)
return mdps, envs
def test_starting_position_randomization(self):
self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room")
start_state_fn = self.base_mdp.get_random_start_state_fn(
random_start_pos=True, rnd_obj_prob_thresh=0.0)
env = OvercookedEnv.from_mdp(self.base_mdp, start_state_fn)
start_state = env.state.players_pos_and_or
for _ in range(3):
env.reset()
print(env)
curr_terrain = env.state.players_pos_and_or
self.assertFalse(np.array_equal(start_state, curr_terrain))
def test_four_player_env_fixed(self):
mdp = OvercookedGridworld.from_layout_name("multiplayer_schelling")
assert mdp.num_players == 4
env = OvercookedEnv.from_mdp(mdp, horizon=16)
a0 = FixedPlanAgent([stay, w, w])
a1 = FixedPlanAgent([stay, stay, e, e, n, n, n, e, interact, n, n, w, w, w, n, interact, e])
a2 = FixedPlanAgent([stay, w, interact, n, n, e, e, e, n, e, n, interact, w])
a3 = FixedPlanAgent([e, interact, n, n, w, w, w, n, interact, e, s])
ag = AgentGroup(a0, a1, a2, a3)
env.run_agents(ag, display=False)
self.assertEqual(
env.state.players_pos_and_or,
(((1, 1), (-1, 0)), ((3, 1), (0, -1)), ((2, 1), (-1, 0)), ((4, 2), (0, 1)))
)
def test_two_coupled_agents(self):
a0 = CoupledPlanningAgent(self.mlp_large)
a1 = CoupledPlanningAgent(self.mlp_large)
agent_pair = AgentPair(a0, a1)
start_state = OvercookedState([P(
(2, 2), n), P((2, 1), n)], {},
order_list=['any'])
env = OvercookedEnv.from_mdp(large_mdp,
start_state_fn=lambda: start_state)
trajectory, time_taken, _, _ = env.run_agents(agent_pair,
include_final_state=True,
display=DISPLAY)
end_state = trajectory[-1][0]
self.assertEqual(end_state.order_list, [])
def get_overcooked_obj_attr(attr,
env=None,
mdp=None,
env_params=None,
mdp_params=None):
"""
returns overcooked object attribute based on its name; used mostly to get state processing (encoding) functions and gym spaces
when receives string parse it to get attribute; format is "env"/"mdp" + "." + method name i.e "env.lossless_state_encoding_mdp"
also support dicts (where replaces strings in values with object attributes)
when receives method/function returns original method; this obviously does not work this way if attr is str/dict
"""
attr_type = type(attr)
if attr_type is str:
name = attr
[obj_name, attr_name] = name.split(".")
if obj_name == "mdp":
if not mdp:
if env:
mdp = env.mdp
else:
mdp = OvercookedGridworld(**mdp_params)
attr = getattr(mdp, attr_name)
elif obj_name == "env":
if not env:
if not mdp:
mdp = OvercookedGridworld(**mdp_params)
env_params = only_valid_named_args(env_params,
OvercookedEnv.from_mdp)
env = OvercookedEnv.from_mdp(mdp, **env_params)
attr = getattr(env, attr_name)
# not tested or used anywhere yet
# elif obj_name in kwargs:
# attr = getattr(kwargs[obj_name], attr_name)
else:
raise ValueError("Unsupported obj attr string " + name)
elif attr_type is dict:
attr = {
k: get_overcooked_obj_attr(v,
env=env,
mdp=mdp,
env_params=env_params,
mdp_params=mdp_params)
for k, v in attr.items()
}
# not tested or used anywhere yet
# elif attr_type in [list, tuple]:
# attr = attr_type(get_overcooked_obj_attr(elem, env=env, mdp=mdp, env_params=env_params,
# mdp_params=mdp_params) for elem in attr)
return attr
def test_two_coupled_agents_coupled_pair(self):
mlp_simple = MediumLevelPlanner.from_pickle_or_compute(
simple_mdp, NO_COUNTERS_PARAMS, force_compute=force_compute)
cp_agent = CoupledPlanningAgent(mlp_simple)
agent_pair = CoupledPlanningPair(cp_agent)
start_state = OvercookedState([P(
(2, 2), n), P((2, 1), n)], {},
order_list=['any'])
env = OvercookedEnv.from_mdp(simple_mdp,
start_state_fn=lambda: start_state)
trajectory, time_taken, _, _ = env.run_agents(agent_pair,
include_final_state=True,
display=DISPLAY)
end_state = trajectory[-1][0]
self.assertEqual(end_state.order_list, [])
def test_two_greedy_human_open_map(self):
scenario_2_mdp = OvercookedGridworld.from_layout_name('scenario2')
mlam = MediumLevelActionManager.from_pickle_or_compute(
scenario_2_mdp, NO_COUNTERS_PARAMS, force_compute=force_compute)
a0 = GreedyHumanModel(mlam)
a1 = GreedyHumanModel(mlam)
agent_pair = AgentPair(a0, a1)
start_state = OvercookedState(
[P((8, 1), s), P((1, 1), s)], {},
all_orders=scenario_2_mdp.start_all_orders)
env = OvercookedEnv.from_mdp(scenario_2_mdp,
start_state_fn=lambda: start_state,
horizon=100)
trajectory, time_taken, _, _ = env.run_agents(agent_pair,
include_final_state=True,
display=DISPLAY)
def test_one_coupled_one_greedy_human(self):
# Even though in the first ~10 timesteps it seems like agent 1 is wasting time
# it turns out that this is actually not suboptimal as the true bottleneck is
# going to be agent 0 later on (when it goes to get the 3rd onion)
a0 = GreedyHumanModel(self.mlp_large)
a1 = CoupledPlanningAgent(self.mlp_large)
agent_pair = AgentPair(a0, a1)
start_state = OvercookedState([P(
(2, 1), s), P((1, 1), s)], {},
order_list=['onion'])
env = OvercookedEnv.from_mdp(large_mdp,
start_state_fn=lambda: start_state)
trajectory, time_taken, _, _ = env.run_agents(agent_pair,
include_final_state=True,
display=DISPLAY)
end_state = trajectory[-1][0]
self.assertEqual(end_state.order_list, [])
def test_agents_on_open_map(self):
scenario_2_mdp = OvercookedGridworld.from_layout_name('scenario2')
mlam = MediumLevelActionManager.from_pickle_or_compute(scenario_2_mdp, NO_COUNTERS_PARAMS, force_compute=force_compute)
agent_pairs = [
AgentPair(GreedyHumanModel(mlam), GreedyHumanModel(mlam)),
AgentPair(SimpleGreedyHumanModel(mlam), SimpleGreedyHumanModel(mlam)),
AgentPair(RandomAgent(all_actions=True), RandomAgent(all_actions=True)),
AgentPair(RandomAgent(all_actions=False), RandomAgent(all_actions=False))
]
start_state = OvercookedState(
[P((8, 1), s),
P((1, 1), s)],
{},
all_orders=scenario_2_mdp.start_all_orders
)
for agent_pair in agent_pairs:
env = OvercookedEnv.from_mdp(scenario_2_mdp, start_state_fn=lambda: start_state, horizon=100)
trajectory, time_taken, _, _ = env.run_agents(agent_pair, include_final_state=True, display=DISPLAY)
def test_two_greedy_human_open_map(self):
scenario_2_mdp = OvercookedGridworld.from_layout_name(
'scenario2', start_order_list=['any'], cook_time=5)
mlp = MediumLevelPlanner.from_pickle_or_compute(
scenario_2_mdp, NO_COUNTERS_PARAMS, force_compute=force_compute)
a0 = GreedyHumanModel(mlp)
a1 = GreedyHumanModel(mlp)
agent_pair = AgentPair(a0, a1)
start_state = OvercookedState([P(
(8, 1), s), P((1, 1), s)], {},
order_list=['onion'])
env = OvercookedEnv.from_mdp(scenario_2_mdp,
start_state_fn=lambda: start_state,
horizon=100)
trajectory, time_taken, _, _ = env.run_agents(agent_pair,
include_final_state=True,
display=DISPLAY)
end_state = trajectory[-1][0]
self.assertEqual(len(end_state.order_list), 0)
def test_transitions_and_environment(self):
bad_state = OvercookedState([P((0, 0), s), P((3, 1), s)], {})
with self.assertRaises(AssertionError):
self.base_mdp.get_state_transition(bad_state, stay)
env = OvercookedEnv.from_mdp(self.base_mdp)
def check_transition(action, expected_path, recompute=False):
# Compute actual values
state = env.state
pred_state, _ = self.base_mdp.get_state_transition(state, action)
new_state, sparse_reward, _, _ = env.step(action)
self.assertEqual(pred_state, new_state,
'\n' + str(pred_state) + '\n' + str(new_state))
# Recompute expected values if desired
if recompute:
actual = {
"state": pred_state.to_dict(),
"reward": sparse_reward
}
save_as_json(actual, expected_path)
# Compute expected values
expected = load_from_json(expected_path)
expected_state = OvercookedState.from_dict(expected['state'])
expected_reward = expected['reward']
# Make sure everything lines up (note __eq__ is transitive)
self.assertTrue(
pred_state.time_independent_equal(expected_state),
'\n' + str(pred_state) + '\n' + str(expected_state))
self.assertEqual(sparse_reward, expected_reward)
expected_path = os.path.join(TESTING_DATA_DIR,
"test_transitions_and_environments",
"expected.json")
# NOTE: set 'recompute=True' if deliberately updating state dynamics
check_transition([n, e], expected_path, recompute=False)
def setUp(self):
if not os.path.exists(self.temp_dir):
os.makedirs(self.temp_dir)
self.base_mdp = OvercookedGridworld.from_layout_name(self.layout_name)
self.mlam = MediumLevelActionManager.from_pickle_or_compute(
self.base_mdp, NO_COUNTERS_PARAMS, force_compute=True, info=False)
self.env = OvercookedEnv.from_mdp(self.base_mdp,
horizon=self.horizon,
info_level=0)
self.starting_state_dict = self.base_mdp.get_standard_start_state(
).to_dict()
outfile = process_human_trials_main(self.infile,
self.temp_dir,
insert_interacts=True,
verbose=False,
forward_port=False,
fix_json=False)
with open(outfile, 'rb') as f:
self.human_data = pickle.load(f)[self.layout_name]
def test_get_gym_space(self):
mdp = OvercookedGridworld.from_layout_name("cramped_room")
mdp_params = mdp.mdp_params
env_params = {"horizon": 100}
env = OvercookedEnv.from_mdp(mdp, **env_params)
example_gym_space_names = ["mdp.multi_hot_orders_encoding_gym_space", "mdp.featurize_state_gym_space",
"mdp.lossless_state_encoding_gym_space"]
example_gym_spaces = [mdp.multi_hot_orders_encoding_gym_space, mdp.featurize_state_gym_space,
mdp.lossless_state_encoding_gym_space]
for space_name, space in zip(example_gym_space_names, example_gym_spaces):
space_from_name = get_gym_space(space_name, env=env)
self.assertEqual(space_from_name, space)
if space_name.split(".")[0] == "mdp":
space_from_name = get_gym_space(space_name, mdp=mdp)
self.assertEqual(space_from_name, space)
space_from_name = get_gym_space(space_name, mdp_params=mdp_params)
self.assertEqual(space_from_name, space)
else:
space_from_name = get_gym_space(space_name, env_params=env_params, mdp_params=mdp_params)
self.assertEqual(space_from_name, space)
expected_space = gym.spaces.Dict({str(i): space for i,space in enumerate(example_gym_spaces)})
actual_space = get_gym_space({str(i): space_name for i, space_name in enumerate(example_gym_space_names)}, env=env)
self.assertEqual(expected_space, actual_space)
from stable_baselines3 import HER, DDPG, DQN, SAC, TD3
from stable_baselines3.her.goal_selection_strategy import GoalSelectionStrategy
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.vec_env.obs_dict_wrapper import ObsDictWrapper
from stable_baselines3.common.monitor import Monitor
import stable_baselines3.common.env_checker
from stable_baselines3.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise
import numpy as np
model_class = TD3 # works also with SAC, DDPG and TD3
mdp = OvercookedGridworld.from_layout_name("cramped_room_single")
base_env = OvercookedEnv.from_mdp(mdp, horizon=1e4)
env = gym.make('Overcooked-single-v0')
env.custom_init(base_env, base_env.lossless_state_encoding_mdp_single)
env = Monitor(env, "./her_overcooked/", allow_early_resets=True)
# Available strategies (cf paper): future, final, episode
# goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
goal_selection_strategy = 'future' # equivalent to GoalSelectionStrategy.FUTURE
# If True the HER transitions will get sampled online
online_sampling = True
# Time limit for the episodes
max_episode_length = 50
action_noise = NormalActionNoise(mean=np.zeros(1), sigma=0.3 * np.ones(1))
def setUp(self):
self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room")
self.env = OvercookedEnv.from_mdp(self.base_mdp, **DEFAULT_ENV_PARAMS)
self.rnd_agent_pair = AgentPair(FixedPlanAgent([]), FixedPlanAgent([]))
np.random.seed(0)
