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])], {}, order_list=['any', 'any'])
env = OvercookedEnv(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_random_layout_feature_types(self):
mandatory_features = {POT, DISH_DISPENSER, SERVING_LOC}
optional_features = {ONION_DISPENSER, TOMATO_DISPENSER}
optional_features_combinations = [{ONION_DISPENSER, TOMATO_DISPENSER}, {ONION_DISPENSER}, {TOMATO_DISPENSER}]
for optional_features_combo in optional_features_combinations:
left_out_optional_features = optional_features - optional_features_combo
used_features = list(optional_features_combo | mandatory_features)
mdp_gen_params = {"prop_feats": 0.9,
"feature_types": used_features,
"prop_empty": 0.1,
"inner_shape": (6, 5),
"display": False,
"start_all_orders" : [
{ "ingredients" : ["onion", "onion", "onion"]}
]}
mdp_fn = LayoutGenerator.mdp_gen_fn_from_dict(mdp_gen_params, outer_shape=(6, 5))
env = OvercookedEnv(mdp_fn, **DEFAULT_ENV_PARAMS)
for _ in range(10):
env.reset()
curr_terrain = env.mdp.terrain_mtx
terrain_features = set.union(*(set(line) for line in curr_terrain))
self.assertTrue(all(elem in terrain_features for elem in used_features)) # all used_features are actually used
if left_out_optional_features:
self.assertFalse(any(elem in terrain_features for elem in left_out_optional_features)) # all left_out optional_features are not used
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:
self.variable_mdp = False
self.mdp_fn = lambda: OvercookedGridworld.from_layout_name(
**mdp_params)
else:
self.variable_mdp = True
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(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(scenario_1_mdp, start_state_fn=lambda: start_state)
env.run_agents(agent_pair, include_final_state=True, display=DISPLAY)
class TestFeaturizations(unittest.TestCase):
def setUp(self):
self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room")
self.mlp = MediumLevelPlanner.from_pickle_or_compute(
self.base_mdp, NO_COUNTERS_PARAMS, force_compute=True)
self.env = OvercookedEnv(self.base_mdp, **DEFAULT_ENV_PARAMS)
self.rnd_agent_pair = AgentPair(GreedyHumanModel(self.mlp),
GreedyHumanModel(self.mlp))
np.random.seed(0)
def test_lossless_state_featurization(self):
trajs = self.env.get_rollouts(self.rnd_agent_pair, num_games=5)
featurized_observations = [[
self.base_mdp.lossless_state_encoding(state) for state in ep_states
] for ep_states in trajs["ep_observations"]]
expected_featurization = load_pickle(
"data/testing/lossless_state_featurization")
self.assertTrue(
np.array_equal(expected_featurization, featurized_observations))
def test_state_featurization(self):
trajs = self.env.get_rollouts(self.rnd_agent_pair, num_games=5)
featurized_observations = [[
self.base_mdp.featurize_state(state, self.mlp)
for state in ep_states
] for ep_states in trajs["ep_observations"]]
expected_featurization = load_pickle(
"data/testing/state_featurization")
self.assertTrue(
np.array_equal(expected_featurization, featurized_observations))
def test_one_player_env(self):
mdp = OvercookedGridworld.from_layout_name("cramped_room_single")
env = OvercookedEnv(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 _check_trajectories_dynamics(trajectories):
for idx in range(len(trajectories["ep_observations"])):
states, actions, rewards = trajectories["ep_observations"][
idx], trajectories["ep_actions"][idx], trajectories[
"ep_rewards"][idx]
mdp_params, env_params = trajectories["mdp_params"][
idx], trajectories["env_params"][idx]
assert len(states) == len(actions) == len(
rewards), "# states {}\t# actions {}\t# rewards {}".format(
len(states), len(actions), len(rewards))
# Checking that actions would give rise to same behaviour in current MDP
simulation_env = OvercookedEnv(
OvercookedGridworld.from_layout_name(**mdp_params),
**env_params)
for i in range(len(states) - 1):
curr_state = states[i]
simulation_env.state = curr_state
next_state, reward, done, info = simulation_env.step(
actions[i])
assert states[
i +
1] == next_state, "States differed (expected vs actual): {}".format(
simulation_env.display_states(states[i + 1],
next_state))
assert rewards[i] == reward, "{} \t {}".format(
rewards[i], reward)
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], {},
order_list=['any', 'any'])
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(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 test_display_phi(self):
mdp0 = OvercookedGridworld.from_layout_name("cramped_room")
mdp_fn = lambda _ignored: mdp0
env = OvercookedEnv(mdp_fn, horizon=20)
env.get_rollouts(self.rnd_agent_pair,
1,
display=True,
display_phi=True)
def setUp(self):
self.base_mdp = OvercookedGridworld.from_layout_name("cramped_room")
self.mlp = MediumLevelPlanner.from_pickle_or_compute(
self.base_mdp, NO_COUNTERS_PARAMS, force_compute=True)
self.env = OvercookedEnv(self.base_mdp, **DEFAULT_ENV_PARAMS)
self.rnd_agent_pair = AgentPair(GreedyHumanModel(self.mlp),
GreedyHumanModel(self.mlp))
np.random.seed(0)
def test_constructor(self):
try:
OvercookedEnv(self.base_mdp, horizon=10)
except Exception as e:
self.fail("Failed to instantiate OvercookedEnv:\n{}".format(e))
with self.assertRaises(TypeError):
OvercookedEnv(self.base_mdp, **{"invalid_env_param": None})
def test_multiple_mdp_env(self):
mdp0 = OvercookedGridworld.from_layout_name("cramped_room")
mdp1 = OvercookedGridworld.from_layout_name("counter_circuit")
mdp_fn = lambda: np.random.choice([mdp0, mdp1])
# Default env
env = OvercookedEnv(mdp_fn, horizon=100)
env.get_rollouts(self.rnd_agent_pair, 5)
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(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(self.base_mdp, start_state_fn)
start_state = env.state.all_objects_list
for _ in range(3):
env.reset()
print(env)
curr_terrain = env.state.all_objects_list
self.assertFalse(np.array_equal(start_state, curr_terrain))
def test_starting_position_randomization(self):
self.base_mdp = OvercookedGridworld.from_layout_name("simple")
start_state_fn = self.base_mdp.get_random_start_state_fn(
random_start_pos=True, rnd_obj_prob_thresh=0.0)
env = OvercookedEnv(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_fixed_plan_agents(self):
a0 = FixedPlanAgent([s, e, n, w])
a1 = FixedPlanAgent([s, w, n, e])
agent_pair = AgentPair(a0, a1)
env = OvercookedEnv(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 check_full_plan(self, start_state, planner, debug=False):
heuristic = Heuristic(planner.mp)
joint_action_plan = planner.get_low_level_action_plan(
start_state,
heuristic.simple_heuristic,
debug=debug,
goal_info=debug)
env = OvercookedEnv(planner.mdp, horizon=1000)
resulting_state, _ = env.execute_plan(start_state,
joint_action_plan,
display=False)
self.assertEqual(len(resulting_state.order_list), 0)
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(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_random_layout(self):
mdp_gen_params = {"prop_feats": (1, 1)}
mdp_fn = LayoutGenerator.mdp_gen_fn_from_dict(**mdp_gen_params)
env = OvercookedEnv(mdp=mdp_fn, **DEFAULT_ENV_PARAMS)
start_terrain = env.mdp.terrain_mtx
for _ in range(3):
env.reset()
print(env)
curr_terrain = env.mdp.terrain_mtx
self.assertFalse(np.array_equal(start_terrain, curr_terrain))
mdp_gen_params = {
"mdp_choices": ['cramped_room', 'asymmetric_advantages']
}
mdp_fn = LayoutGenerator.mdp_gen_fn_from_dict(**mdp_gen_params)
env = OvercookedEnv(mdp=mdp_fn, **DEFAULT_ENV_PARAMS)
layouts_seen = []
for _ in range(10):
layouts_seen.append(env.mdp.terrain_mtx)
env.reset()
all_same_layout = all([
np.array_equal(env.mdp.terrain_mtx, terrain)
for terrain in layouts_seen
])
self.assertFalse(all_same_layout)
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(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_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_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(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_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(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 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 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 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 test_four_player_env_fixed(self):
mdp = OvercookedGridworld.from_layout_name("multiplayer_schelling")
assert mdp.num_players == 4
env = OvercookedEnv(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 mdps_and_envs_from_trajectories(trajectories):
mdps, envs = [], []
for idx in range(len(trajectories["ep_lengths"])):
mdp_params, env_params = trajectories["mdp_params"][idx], trajectories["env_params"][idx]
mdp = OvercookedGridworld.from_layout_name(**mdp_params)
env = OvercookedEnv(mdp, **env_params)
mdps.append(mdp)
envs.append(env)
return mdps, envs
def init_gym_env(bc_params):
env_setup_params = copy.deepcopy(bc_params)
del env_setup_params["data_params"] # Not necessary for setting up env
mdp = OvercookedGridworld.from_layout_name(**bc_params["mdp_params"])
env = OvercookedEnv(mdp, **bc_params["env_params"])
gym_env = gym.make("Overcooked-v0")
mlp = MediumLevelPlanner.from_pickle_or_compute(mdp, NO_COUNTERS_PARAMS, force_compute=False)
gym_env.custom_init(env, featurize_fn=lambda x: mdp.featurize_state(x, mlp))
return gym_env
def __init__(self,
env_params,
mdp_fn,
force_compute=False,
mlam_params=NO_COUNTERS_PARAMS,
debug=False):
"""
env_params (dict): params for creation of an OvercookedEnv
mdp_fn (callable function): a function that can be used to create mdp
force_compute (bool): whether should re-compute MediumLevelActionManager although matching file is found
mlam_params (dict): the parameters for mlam, the MediumLevelActionManager
debug (bool): whether to display debugging information on init
"""
assert callable(
mdp_fn), "mdp generating function must be a callable function"
env_params["mlam_params"] = mlam_params
self.mdp_fn = mdp_fn
self.env = OvercookedEnv(self.mdp_fn, **env_params)
self.force_compute = force_compute