Base Network schedule_timesteps. After this many timesteps pass final_p is returned. Parameters ---------- inverse_value_transform: Any A function that maps value supports into value scalars inverse_reward_transform: Any A funct
(self, inverse_value_transform, inverse_reward_transform, lstm_hidden_size)
| 52 | |
| 53 | class BaseNet(nn.Module): |
| 54 | def __init__(self, inverse_value_transform, inverse_reward_transform, lstm_hidden_size): |
| 55 | """Base Network |
| 56 | schedule_timesteps. After this many timesteps pass final_p is |
| 57 | returned. |
| 58 | Parameters |
| 59 | ---------- |
| 60 | inverse_value_transform: Any |
| 61 | A function that maps value supports into value scalars |
| 62 | inverse_reward_transform: Any |
| 63 | A function that maps reward supports into value scalars |
| 64 | lstm_hidden_size: int |
| 65 | dim of lstm hidden |
| 66 | """ |
| 67 | super(BaseNet, self).__init__() |
| 68 | self.inverse_value_transform = inverse_value_transform |
| 69 | self.inverse_reward_transform = inverse_reward_transform |
| 70 | self.lstm_hidden_size = lstm_hidden_size |
| 71 | |
| 72 | def prediction(self, state): |
| 73 | raise NotImplementedError |
nothing calls this directly
no outgoing calls
no test coverage detected