Created
August 9, 2016 00:52
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CEM rollout
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| def rollout(self, w, render): | |
| """ | |
| Plays one episode to `max_num_steps` or a terminal state, given a weight vector w. | |
| Returns a scalar of the reward sum of the episode. | |
| """ | |
| self._update_network(w) | |
| observation = self.env.reset() | |
| cart_position, pole_angle, cart_velocity, angle_rate_of_change = observation | |
| total_reward = 0.0 | |
| for _ in xrange(self.max_num_steps - 1): | |
| action = self.pred_network.calc_action(observation.reshape(1,4)) | |
| observation, reward, is_terminal, info = self.env.step(action) | |
| total_reward += reward | |
| if is_terminal: | |
| break | |
| if render: | |
| self.env.render() | |
| return total_reward | |
| def _update_network(self, w): | |
| """ | |
| Updates the network with the weight vector `w`. This side effects the | |
| existing `pred_network`. | |
| """ | |
| assign_w = self.pred_network.w.assign(w[:-1].reshape(4,1)) | |
| assign_b = self.pred_network.b.assign([w[-1]]) | |
| ops = tf.group(assign_w, assign_b, name="update") | |
| self.sess.run(ops) |
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