| 47 | self.eval() |
| 48 | |
| 49 | def act(self, inputs, uav_aoi, uav_snr, uav_compl, uav_tc_compl, temporal_hidden_state=None, mask=None, |
| 50 | spatial_hidden_state=None): |
| 51 | if params.use_rnn: |
| 52 | if params.use_spatial_att: |
| 53 | obs_feature, temporal_hidden_state, spatial_hidden_state, = self.base(inputs, temporal_hidden_state, |
| 54 | mask, spatial_hidden_state) |
| 55 | else: |
| 56 | obs_feature, temporal_hidden_state = self.base(inputs, temporal_hidden_state, mask) |
| 57 | |
| 58 | else: |
| 59 | obs_feature = self.base(inputs) |
| 60 | full_obs_feature = torch.cat( |
| 61 | [obs_feature, uav_aoi.float(), uav_snr.float(), uav_compl.float(), uav_tc_compl.float()], dim=1) |
| 62 | value = self.critic(full_obs_feature) |
| 63 | dist_dia = self.dist_dia(full_obs_feature) |
| 64 | action_dia = dist_dia.sample() |
| 65 | |
| 66 | action_log_probs_dia = dist_dia.log_probs(action_dia) |
| 67 | |
| 68 | # print('action log probs dia', action_log_probs_dia.mean()) |
| 69 | if params.use_rnn: |
| 70 | if params.use_spatial_att: |
| 71 | return value, action_dia, action_log_probs_dia, temporal_hidden_state, spatial_hidden_state |
| 72 | else: |
| 73 | return value, action_dia, action_log_probs_dia, temporal_hidden_state |
| 74 | |
| 75 | else: |
| 76 | return value, action_dia, action_log_probs_dia |
| 77 | |
| 78 | def get_value(self, inputs, uav_aoi, uav_snr, uav_compl, uav_tc_compl, temporal_hidden_state=None, mask=None, |
| 79 | spatial_hidden_state=None): |