Initial release. Hello world :).

examples/agents/tabular_q_agent.py

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+class TabularQAgent(object):
+    """
+    Agent implementing tabular Q-learning.
+    """
+
+    def __init__(self, observation_space, action_space, **userconfig):
+        if not isinstance(observation_space, discrete.Discrete):
+            raise UnsupportedSpace('Observation space {} incompatible with {}. (Only supports Discrete observation spaces.)'.format(observation_space, self))
+        if not isinstance(action_space, discrete.Discrete):
+            raise UnsupportedSpace('Action space {} incompatible with {}. (Only supports Discrete action spaces.)'.format(action_space, self))
+        self.observation_space = observation_space
+        self.action_space = action_space
+        self.action_n = action_space.n
+        self.config = {
+            "init_mean" : 0.0,      # Initialize Q values with this mean
+            "init_std" : 0.0,       # Initialize Q values with this standard deviation
+            "learning_rate" : 0.1,
+            "eps": 0.05,            # Epsilon in epsilon greedy policies
+            "discount": 0.95,
+            "n_iter": 10000}        # Number of iterations
+        self.config.update(userconfig)
+        self.q = defaultdict(lambda: self.config["init_std"] * np.random.randn(self.action_n) + self.config["init_mean"])
+
+    def act(self, observation, eps=None):
+        if eps is None:
+            eps = self.config["eps"]
+        # epsilon greedy.
+        action = np.argmax(self.q[observation.item()]) if np.random.random() > eps else self.action_space.sample()
+        return action
+
+    def learn(self, env):
+        config = self.config
+        obs = env.reset()
+        q = self.q
+        for t in xrange(config["n_iter"]):
+            action, _ = self.act(obs)
+            obs2, reward, done, _ = env.step(action)
+            future = 0.0
+            if not done:
+                future = np.max(q[obs2.item()])
+            q[obs.item()][action] -= \
+                self.config["learning_rate"] * (q[obs.item()][action] - reward - config["discount"] * future)
+
+            obs = obs2