(k_end - k_start) * reward_x +
(k_start * reward_sup - reward_inf * k_end)) / (reward_sup - reward_inf)
kends = [1., 1.25, 1.5, 1.75, 2., 2.25, 2.5, 2.75, 3]
current_model = NoisyDQN(env.observation_space.shape[0], env.action_space.n,
env)
target_model = NoisyDQN(env.observation_space.shape[0], env.action_space.n,
env)
if USE_CUDA:
current_model = current_model.cuda()
target_model = target_model.cuda()
optimizer = optim.Adam(current_model.parameters(), lr=0.0001)
replay_buffer = tl.BaseReplayBuffer(10000)
def update_target(current_model, target_model):
target_model.load_state_dict(current_model.state_dict())
update_target(current_model, target_model)
losses_all = []
rewards_all = []
for k_end in kends:
k_start = 0
reward_inf = -20
reward_sup = 0
env_id = game_name
env = make_atari(env_id)
env = wrap_deepmind(env)
env = wrap_pytorch(env)
current_model = CnnNoisyDQN(env.observation_space.shape, env.action_space.n, env)
target_model = CnnNoisyDQN(env.observation_space.shape, env.action_space.n, env)
if USE_CUDA:
current_model = current_model.cuda()
target_model = target_model.cuda()
optimizer = optim.Adam(current_model.parameters(), lr = learningRate)
replay_buffer = tl.BaseReplayBuffer(capacity)
def update_target(current_model, target_model):
target_model.load_state_dict(current_model.state_dict())
update_target(current_model, target_model)
losses_all = []
rewards_all = []
for i in range(arvg_num):
losses = []
all_rewards = []
episode_reward = 0
state = env.reset()
current_model = CnnNoisyDQN(env.observation_space.shape, env.action_space.n, env)
target_model = CnnNoisyDQN(env.observation_space.shape, env.action_space.n, env)
