def __init__(self, chain_length):
super(ActionChainAgent, self).__init__(name='ActionChainAgent',
version='1.2')
self.q = dict() # state-action values: q[state][action]
self.chain = CircularList(chain_length)
# e=1 until frame 5k, then interpolate down to e=0.05 in frame 10k,
# and keep it there for the remaining time
self.e_params = (5000, 10000, 1.0, 0.05)
self.e = 0.5
self.nframes = 0
self.learning_rate = 0.1
self.discount = 0.9
self.last_action = None
def __init__(self, n_frames_per_action=4,
trace_type='replacing',
learning_rate=0.001,
discount=0.99,
lambda_v=0.5,
record=False):
super(SarsaAgent, self).__init__(name='Sarsa', version='1')
self.n_frames_per_action = n_frames_per_action
self.epsilon = LinearInterpolationManager([(0, 1.0), (1e4, 0.005)])
self.action_repeat_manager = RepeatManager(n_frames_per_action - 1)
self.trace_type = trace_type
self.learning_rate = learning_rate
self.lambda_v = lambda_v
self.discount = discount
self.a_ = 0
self.s_ = 0
self.r_ = 0
self.q_vals = None
self.e_vals = None
self.n_goals = 0
self.n_greedy = 0
self.n_random = 0
self.record = record
if record:
# 5 action, 3 states
# => q_vals.shape == (5, 3)
# e_vals.shape == (5, 3)
# sarsa.shape == (5, 1)
self.mem = CircularList(100000)
self.n_rr = 0
self.n_sa = 0
self.n_episode = 0
def __init__(self, n_frames_per_action=4):
super(SLAgent, self).__init__(name='SL', version='1')
self.experience = CircularList(1000)
self.epsilon = LinearInterpolationManager([(0, 1.0), (1e4, 0.1)])
self.action_repeat_manager = RepeatManager(n_frames_per_action - 1)
