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adaptive_ensemble.py
"""
from collections import deque
import numpy as np
class AdaptiveEnsembler:
def __init__(self, pred_action_horizon, adaptive_ensemble_alpha=0.0):
self.pred_action_horizon = pred_action_horizon
self.action_history = deque(maxlen=self.pred_action_horizon)
self.adaptive_ensemble_alpha = adaptive_ensemble_alpha
def reset(self):
self.action_history.clear()
def ensemble_action(self, cur_action):
self.action_history.append(cur_action)
num_actions = len(self.action_history)
if cur_action.ndim == 1:
curr_act_preds = np.stack(self.action_history)
else:
curr_act_preds = np.stack(
[pred_actions[i] for (i, pred_actions) in zip(range(num_actions - 1, -1, -1), self.action_history)]
)
# calculate cosine similarity between the current prediction and all previous predictions
ref = curr_act_preds[num_actions - 1, :]
previous_pred = curr_act_preds
dot_product = np.sum(previous_pred * ref, axis=1)
norm_previous_pred = np.linalg.norm(previous_pred, axis=1)
norm_ref = np.linalg.norm(ref)
cos_similarity = dot_product / (norm_previous_pred * norm_ref + 1e-7)
# compute the weights for each prediction
weights = np.exp(self.adaptive_ensemble_alpha * cos_similarity)
weights = weights / weights.sum()
# compute the weighted average across all predictions for this timestep
cur_action = np.sum(weights[:, None] * curr_act_preds, axis=0)
return cur_action
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