class ValSearchPolicy(Policy):
"""Searches for the maximum reward path using a model."""
def __init__(self,
V,
replan=False,
epsilon=0,
noise=1,
anneal=1,
**kwargs):
super().__init__(**kwargs)
self.V = V
self.replan = replan
self.epsilon = epsilon
self.noise = noise
self.anneal = anneal
self.history = None
self.model = None
self.plan = None
def start_episode(self, state):
self.history = Counter()
self.model = Model(self.env)
self.plan = iter(()) # start with no plan
def finish_episode(self, trace):
self.ep_trace['berries'] = self.env._observe()[-1]
def act(self, state):
# return self.env.action_space.sample()
self.history[state] += 1
try:
if self.replan:
raise StopIteration()
else:
return next(self.plan)
except StopIteration:
self.plan = iter(self.make_plan(state))
return next(self.plan)
def make_plan(self, state, expansions=2000):
Node = namedtuple('Node', ('state', 'path', 'reward', 'done'))
env = self.env
V = memoize(self.V.predict)
self.node_history = []
def eval_node(node, noisy=False):
if not node.path:
return np.inf # the empty plan has infinite cost
obs = env._observe(node.state)
noise = np.random.rand() * (
self.noise * self.anneal**self.i_episode) if noisy else 0
value = 0 if node.done else V(obs)[0]
boredom = -0.1 * self.history[obs]
score = node.reward + value + noise + boredom
return -score
start = Node(env._state, [], 0, False)
frontier = PriorityQueue(key=eval_node)
frontier.push(start)
reward_to_state = defaultdict(lambda: -np.inf)
reward_to_state[start.state] = 0
best_finished = start
def expand(node):
nonlocal best_finished
best_finished = min((best_finished, node), key=eval_node)
s0, p0, r0, _ = node
for a, s1, r, done in self.model.options(s0):
node1 = Node(s1, p0 + [a], r0 + r, done)
if node1.reward <= reward_to_state[s1]:
continue # cannot be better than an existing node
self.node_history.append({
'path':
node1.path,
'r':
node1.reward,
'b':
self.env._observe(node1.state)[-1],
'v':
-eval_node(node1)
})
reward_to_state[s1] = node1.reward
if done:
best_finished = min((best_finished, node1), key=eval_node)
else:
frontier.push(node1)
for i in range(expansions):
if frontier:
expand(frontier.pop())
else:
break
if frontier:
plan = min(best_finished, frontier.pop(), key=eval_node)
else:
plan = best_finished
# choices = concat([completed, map(get(1), take(100, frontier))])
# plan = min(choices, key=eval_node(noisy=True))
self.log(
i,
len(plan.path),
-round(eval_node(plan, noisy=False), 2),
plan.done,
)
# self._trace['paths'].append(plan.path)
return plan.path
class Astar(Policy):
"""A* search finds the shortest path to a goal."""
def __init__(self, heuristic):
assert 0 # this implementation is incorrect
super().__init__()
self.heuristic = heuristic
self.plan = iter(())
def start_episode(self, state):
self.history = Counter()
self.model = Model(self.env)
def act(self, state):
# return self.env.action_space.sample()
self.history[state] += 1
try:
return next(self.plan)
except StopIteration:
self.plan = iter(self.make_plan(state))
return next(self.plan)
def eval_node(self, node):
if not node.path:
return np.inf # the empty plan has infinite cost
obs = self.env._observe(node.state)
value = 0 if node.done else self.heuristic(self.env, obs)
boredom = -0.1 * self.history[obs]
score = node.reward + value + boredom
return -score
def make_plan(self, state, expansions=5000):
Node = namedtuple('Node', ('state', 'path', 'reward', 'done'))
eval_node = self.eval_node
start = Node(self.env._state, [], 0, False)
frontier = PriorityQueue(key=eval_node)
frontier.push(start)
reward_to_state = defaultdict(lambda: -np.inf)
# import IPython; IPython.embed()
best_finished = start
def expand(node):
# print(node.state, node.reward, self.rts[node.state], V(env._observe(node.state)))
# time.sleep(0.1)
nonlocal best_finished
# best_finished = min((best_finished, node), key=eval_node)
s0, p0, r0, _ = node
for a, s1, r, done in self.model.options(s0):
node1 = Node(s1, p0 + [a], r0 + r, done)
if node1.reward <= reward_to_state[s1]:
# print('abandon')
pass
continue # cannot be better than an existing node
# self.save('node', node)
reward_to_state[s1] = node1.reward
if done:
best_finished = min((best_finished, node1), key=eval_node)
else:
frontier.push(node1)
for i in range(expansions):
self.save('frontier', [n[1].state for n in frontier])
if frontier:
expand(frontier.pop())
else:
break
if frontier:
# plan = min(best_finished, frontier.pop(), key=eval_node)
plan = frontier.pop()
raise RuntimeError('No plan found.')
else:
plan = best_finished
# choices = concat([completed, map(get(1), take(100, frontier))])
# plan = min(choices, key=eval_node(noisy=True))
# self.log(
# i,
# len(plan.path),
# -round(eval_node(plan, noisy=False), 2),
# plan.done,
# )
# self._trace['paths'].append(plan.path)
self.save('plan', plan)
return plan.path
class MetaBestFirstSearchEnv(gym.Env):
"""A meta-MDP for best first search with a deterministic transition model."""
Node = namedtuple('Node', ('state', 'path', 'reward', 'done'))
State = namedtuple('State', ('frontier', 'reward_to_state', 'best_done'))
TERM = 'TERM'
def __init__(self, env, eval_node, expansion_cost=0.01):
super().__init__()
self.env = env
self.expansion_cost = -abs(expansion_cost)
# This guy interacts with the external environment, what a chump!
self.surface_agent = Agent()
self.surface_agent.register(self.env)
self.eval_node = eval_node
def _reset(self):
self.env.reset()
self.model = Model(
self.env) # warning: this breaks if env resets again
start = self.Node(self.env._state, [], 0, False)
frontier = PriorityQueue(key=self.eval_node(
noisy=True)) # this is really part of the Meta Policy
frontier.push(start)
reward_to_state = defaultdict(lambda: -np.inf)
best_done = None
# Warning: state is mutable (and we mutate it!)
self._state = self.State(frontier, reward_to_state, best_done)
return self._state
def _step(self, action):
"""Expand a node in the frontier."""
if action is self.TERM:
# The return of one episode in the external env is
# one reward in the MetaSearchEnv.
trace = self._execute_plan()
external_reward = trace['return']
return None, external_reward, True, {'trace': trace}
else:
return self._expand_node(action), self.expansion_cost, False, {}
def _execute_plan(self):
frontier, reward_to_state, best_done = self._state
if not best_done:
raise RuntimeError('Cannot make plan.')
policy = FixedPlanPolicy(best_done.path)
self.surface_agent.register(policy)
trace = self.surface_agent.run_episode(reset=False)
return trace
# elif frontier:
# plan = min(best_done, frontier.pop(), key=eval_node)
# plan = frontier.pop()
def _expand_node(self, node):
frontier, reward_to_state, best_done = self._state
s0, p0, r0, _ = node
for a, s1, r, done in self.model.options(s0):
node1 = self.Node(s1, p0 + [a], r0 + r, done)
if node1.reward <= reward_to_state[s1] - 0.002:
continue # cannot be better than an existing node
reward_to_state[s1] = node1.reward
if done:
best_done = max((best_done, node1),
key=self.eval_node(noisy=False))
else:
frontier.push(node1)
self._state = self.State(frontier, reward_to_state, best_done)
return self._state
