def run_episode(config, parameters, actions, counters, ws, gw, mc, episode):
logging.debug("starting new episode")
flower_picked = False # can actually skip this and use reward value for while loop, but this makes it easier to see what's going on.
reward = 0
# reset action for current episode
action = None
#episode action count
ep_actioncount = 0
# episode will continue until a flower is picked
while not flower_picked:
#select random action
action = np.random.choice(actions.keys())
logging.debug("ACTION: %s" % (action))
# reward is 10 if flower was picked else 0
reward = actionutils.take_action(action, config['walk_stride'], config['look_angle'], mc)
# increase number of actions taken
counters['actioncount'] += 1
# allow action animation to finish before observing new state
time.sleep(0.8)
# send q-value to websocket api server if that setting is enabled
message = { 'action': action, 'q_value': actions[action].value, 'step': counters['actioncount'], 'episode': episode, 'round': counters['roundcount'] }
if config['graph_server']: ws.send(json.dumps(message))
# episode is over if flower was picked
if reward != 0:
flower_picked = True
counters['flowercount'] += 1
ep_actioncount += 1
logging.debug("actions taken this episode: %s" % (ep_actioncount))
# reset action before next step
action = None
def run_episode(config, parameters, actions, counters, ws, gw, mc, episode):
logging.debug("starting new episode")
flower_picked = False # can actually skip this and use reward value for while loop, but this makes it easier to see what's going on.
reward = 0
#init states
states = []
# observe initial state, states[0], and add it to states
states.append(observe_state(gw))
# reset action for current episode
action = None
#episode action count
ep_actioncount = 0
# episode will continue until a flower is picked
while not flower_picked:
action_strategy = ""
# check if enough red is in the frame for innate chop
redrum = is_it_red(states[0])
if redrum:
if np.random.uniform(0, 1) < parameters['chop_prob']:
action = 'chop'
action_strategy = "innate"
logging.debug("INNATE: chop")
# if we still don't have an action we should explore/exploit
if action == None:
# select action depending on explore/exploit probability epsilon
if np.random.uniform(0, 1) < parameters['epsilon']:
#explore
action = np.random.choice(actions.keys())
action_strategy = "explore"
logging.debug("EXPLORE: %s" % (action))
else:
# exploit
action = select_optimal_action(actions)
action_strategy = "exploit"
logging.debug("EXPLOIT: %s" % (action))
# reward is 10 if flower was picked else 0
reward = actionutils.take_action(action, config['walk_stride'],
config['look_angle'], mc)
# increase number of actions taken
counters['actioncount'] += 1
# allow action animation to finish before observing new state
time.sleep(0.8)
# observe new state, states[1]
states.append(observe_state(gw))
actions[action].update(
states, reward,
actions) #update function will do forward and backprop for weights
# update action datastream
update_action_datastream(action, actions, counters, action_strategy,
episode)
# send q-value to websocket api server if that setting is enabled
message = {
'action': action,
'q_value': actions[action].value,
'step': counters['actioncount'],
'episode': episode,
'round': counters['roundcount']
}
if config['graph_server']: ws.send(json.dumps(message))
# new state, states[1], becomes states[0] by deleting states[0]
del states[0]
# episode is over if flower was picked
if reward != 0:
flower_picked = True
counters['flowercount'] += 1
ep_actioncount += 1
logging.debug("actions taken this episode: %s" % (ep_actioncount))
# reset action before next step
action = None
def run_episode(config, parameters, actions, counters, ws, gw, mc, episode):
logging.debug("starting new episode")
flower_picked = False # can actually skip this and use reward value for while loop, but this makes it easier to see what's going on.
reward = 0
#init states
states = []
# observe initial state, states[0], and add it to states
states.append( observe_state(gw) )
# reset action for current episode
action = None
#episode action count
ep_actioncount = 0
# episode will continue until a flower is picked
while not flower_picked:
action_strategy = ""
# check if enough red is in the frame for innate chop
redrum = is_it_red(states[0])
if redrum:
if np.random.uniform(0,1) < parameters['chop_prob']:
action = 'chop'
action_strategy = "innate"
logging.debug("INNATE: chop")
# if we still don't have an action we should explore/exploit
if action == None:
# select action depending on explore/exploit probability epsilon
if np.random.uniform(0,1) < parameters['epsilon']:
#explore
action = np.random.choice(actions.keys())
action_strategy = "explore"
logging.debug("EXPLORE: %s" % (action))
else:
# exploit
action = select_optimal_action(actions)
action_strategy = "exploit"
logging.debug("EXPLOIT: %s" % (action))
# reward is 10 if flower was picked else 0
reward = actionutils.take_action(action, config['walk_stride'], config['look_angle'], mc)
# increase number of actions taken
counters['actioncount'] += 1
# allow action animation to finish before observing new state
time.sleep(0.8)
# observe new state, states[1]
states.append( observe_state(gw) )
actions[action].update(states, reward, actions) #update function will do forward and backprop for weights
# update action datastream
update_action_datastream(action,actions,counters,action_strategy,episode)
# send q-value to websocket api server if that setting is enabled
message = { 'action': action, 'q_value': actions[action].value, 'step': counters['actioncount'], 'episode': episode, 'round': counters['roundcount'] }
if config['graph_server']: ws.send(json.dumps(message))
# new state, states[1], becomes states[0] by deleting states[0]
del states[0]
# episode is over if flower was picked
if reward != 0:
flower_picked = True
counters['flowercount'] += 1
ep_actioncount += 1
logging.debug("actions taken this episode: %s" % (ep_actioncount))
# reset action before next step
action = None
