modularized DQN

task.py

@@ -0,0 +1,42 @@
+import gym
+import sys
+from dqn_agent import *
+
+class BasicTask:
+    def transfer_state(self, state):
+        return state
+
+    def reset(self):
+        return self.transfer_state(self.env.reset())
+
+    def step(self, action):
+        next_state, reward, done, info = self.env.step(action)
+        next_state = self.transfer_state(next_state)
+        return next_state, reward, done, info
+
+class MountainCar(BasicTask):
+    state_space_size = 2
+    action_space_size = 3
+    name = 'MountainCar-v0'
+    def __init__(self):
+        self.env = gym.make(self.name)
+        self.env._max_episode_steps = sys.maxsize
+
+if __name__ == '__main__':
+    task = MountainCar()
+    optimizer_fn = lambda name: tf.train.GradientDescentOptimizer(name=name, learning_rate=0.01)
+    network_fn = lambda name: Network(name, task.state_space_size,
+                                      task.action_space_size, optimizer_fn, tf.random_normal_initializer())
+    policy_fn = lambda: GreedyPolicy(epsilon=0.5, decay_factor=0.95, min_epsilon=0.1)
+    replay_fn = lambda: Replay(memory_size=10000, batch_size=10)
+    agent = DQNAgent('mountain-car', task, network_fn, policy_fn, replay_fn,
+                     discount=0.99, step_limit=5000, target_network_update_freq=1000)
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        ep = 0
+        while True:
+            ep += 1
+            reward = agent.episode(sess)
+            print 'episode %d: %f' % (ep, reward)
+            if reward > -110:
+                break