test greedy agent

agent/__init__.py

@@ -1 +1,2 @@
-from .util import create_agent_helper
+from .util import create_agent_helper
+from .greedy import GreedyAgent

agent/greedy.py

@@ -0,0 +1,58 @@
+from recsim.agent import AbstractEpisodicRecommenderAgent
+import numpy as np
+
+class GreedyAgent(AbstractEpisodicRecommenderAgent):
+  def __init__(self, sess, observation_space, action_space, eval_mode, summary_writer):
+    super(GreedyAgent, self).__init__(action_space, summary_writer)
+    self._num_candidates = int(action_space.nvec[0])
+    self._W = np.array([[3, 1.5, 0.5]] * self._num_candidates)
+    assert self._slate_size == 1
+  def begin_episode(self, observation=None):
+    user = observation['user']
+    docs = observation['doc']
+    if 'W' in user: # use observable W
+      self._W = user['W']
+    else:
+      w = []
+      for doc_id in docs:
+        w.append(docs[doc_id])
+      self._W = np.array(w).reshape((-1, 3))
+      print("agent W:", self._W)
+    self._episode_num += 1
+    return self.step(0, observation)
+  def step(self, reward, observation):
+    docs = observation['doc']
+    user = observation['user']
+
+    base_pr = self.calc_prs(user['time'], user['last_review'], user['history'], self._W)
+    # np.exp(-last_review / np.exp(np.dot(W, x))).squeeze()
+    max_pr = -self._num_candidates
+    max_id = 0
+    for did in docs:
+      doc_id = int(did)
+      last_review = user['last_review'].copy()
+      history =  user['history'].copy()
+      last_review[doc_id] = user['time']
+      time = user['time'] + 1
+
+      history[doc_id][0] += 1
+      history[doc_id][1] += 1
+      pr1 = self.calc_prs(time, last_review, history, self._W)
+      history[doc_id][1] -= 1
+      history[doc_id][2] += 1
+      pr2 = self.calc_prs(time, last_review, history, self._W)
+      pr = (pr1 + pr2) / 2 - base_pr
+      sum_pr = np.sum(pr)
+      if sum_pr > max_pr:
+        max_pr = sum_pr
+        max_id = doc_id
+      # print("pr1", pr1)
+      # print("pr2", pr2)
+      # print("pr0", base_pr)
+    print(f"choose doc{max_id} with marginal gain {max_pr}")
+    return [max_id]
+  def calc_prs(self, train_time, last_review, history, W):
+    last_review = train_time - last_review
+    mem_param = np.exp(np.einsum('ij,ij->i', history, W))
+    pr = np.exp(-last_review / mem_param)
+    return pr