device = torch.device("cuda:0")
# device = torch.device(c.CPU)
action_repeat = 1
num_frames = 1
aux_reward = MountainCarContinuousAuxiliaryReward()
num_tasks = 1 + aux_reward.num_auxiliary_rewards
experiment_setting = {
# Auxiliary Tasks
c.AUXILIARY_TASKS: {},
# Buffer
c.BUFFER_PREPROCESSING:
gt.AsType(),
c.BUFFER_SETTING: {
c.KWARGS: {
c.MEMORY_SIZE: 50000 // action_repeat,
c.OBS_DIM: (obs_dim, ),
c.H_STATE_DIM: (1, ),
c.ACTION_DIM: (action_dim, ),
c.REWARD_DIM: (num_tasks, ),
c.INFOS: {
c.MEAN: ((action_dim, ), np.float32),
c.VARIANCE: ((action_dim, ), np.float32),
c.ENTROPY: ((action_dim, ), np.float32),
c.LOG_PROB: ((1, ), np.float32),
c.VALUE: ((1, ), np.float32),
c.DISCOUNTING: ((1, ), np.float32)
},
min_action = -np.ones(action_dim)
max_action = np.ones(action_dim)
# device = torch.device(c.CPU)
device = torch.device("cuda:0")
action_repeat = 1
num_frames = 1
memory_size = max_total_steps = 1000000 // action_repeat
experiment_setting = {
# Auxiliary Tasks
c.AUXILIARY_TASKS: {},
# Buffer
c.BUFFER_PREPROCESSING: gt.AsType(),
c.BUFFER_SETTING: {
c.KWARGS: {
c.MEMORY_SIZE: memory_size,
c.OBS_DIM: (obs_dim,),
c.H_STATE_DIM: (1,),
c.ACTION_DIM: (action_dim,),
c.REWARD_DIM: (1,),
c.INFOS: {c.MEAN: ((action_dim,), np.float32),
c.VARIANCE: ((action_dim,), np.float32),
c.ENTROPY: ((action_dim,), np.float32),
c.LOG_PROB: ((1,), np.float32),
c.VALUE: ((1,), np.float32),
c.DISCOUNTING: ((1,), np.float32)},
c.CHECKPOINT_INTERVAL: 0,
c.CHECKPOINT_PATH: None,
# device = torch.device(c.CPU)
action_repeat = 1
num_frames = 1
hidden_state_dim = 128
memory_size = 1000000
max_total_steps = 1000000
experiment_setting = {
# Auxiliary Tasks
c.AUXILIARY_TASKS: {},
# Buffer
c.BUFFER_PREPROCESSING:
gt.Compose([gt.AsType()]),
c.BUFFER_SETTING: {
c.KWARGS: {
c.MEMORY_SIZE: memory_size,
c.OBS_DIM: (obs_dim, ),
c.H_STATE_DIM: (hidden_state_dim * 2, ),
c.ACTION_DIM: (action_dim, ),
c.REWARD_DIM: (1, ),
c.INFOS: {
c.MEAN: ((action_dim, ), np.float32),
c.VARIANCE: ((action_dim, ), np.float32),
c.ENTROPY: ((action_dim, ), np.float32),
c.LOG_PROB: ((1, ), np.float32),
c.VALUE: ((1, ), np.float32),
c.DISCOUNTING: ((1, ), np.float32)
},
