What's wrong with Dyna-Q ? (Dyna-Q vs Q-learning)

Question

I implemented the Q-learning algorithm and used it on FrozenLake-v0 on OpenAI gym. I am getting 185 total rewards during training and 7333 total rewards during testing in 10000 episodes. Is this good?

Also I tried the Dyna-Q algorithm. But it is giving worse performance than Q-learning. Approx. 200 total rewards during training and 700-900 total rewards during testing in 10000 episodes with 50 planning steps.

Why is this happening?

Below is the code. Is something wrong with the code?

# Setup
env = gym.make('FrozenLake-v0')

epsilon = 0.9
lr_rate = 0.1
gamma = 0.99
planning_steps = 0

total_episodes = 10000
max_steps = 100

Training and testing():

while t < max_steps:
    action = agent.choose_action(state)  
    state2, reward, done, info = agent.env.step(action)  
    # Removed in testing
    agent.learn(state, state2, reward, action)
    agent.model.add(state, action, state2, reward)
    agent.planning(planning_steps)
    # Till here
    state = state2

def add(self, state, action, state2, reward):
        self.transitions[state, action] = state2
        self.rewards[state, action] = reward

def sample(self, env):
    state, action = 0, 0
    # Random visited state
    if all(np.sum(self.transitions, axis=1)) <= 0:
        state = np.random.randint(env.observation_space.n)
    else:
        state = np.random.choice(np.where(np.sum(self.transitions, axis=1) > 0)[0])

    # Random action in that state
    if all(self.transitions[state]) <= 0:
        action = np.random.randint(env.action_space.n)
    else:    
        action = np.random.choice(np.where(self.transitions[state] > 0)[0])
    return state, action

def step(self, state, action):
    state2 = self.transitions[state, action]
    reward = self.rewards[state, action]
    return state2, reward

def choose_action(self, state):
    if np.random.uniform(0, 1) < epsilon:
        return self.env.action_space.sample()
    else:
        return np.argmax(self.Q[state, :])

def learn(self, state, state2, reward, action):
    # predict = Q[state, action]
    # Q[state, action] = Q[state, action] + lr_rate * (target - predict)
    target = reward + gamma * np.max(self.Q[state2, :])
    self.Q[state, action] = (1 - lr_rate) * self.Q[state, action] + lr_rate * target

def planning(self, n_steps):
    # if len(self.transitions)>planning_steps:
    for i in range(n_steps):
        state, action =  self.model.sample(self.env)
        state2, reward = self.model.step(state, action)
        self.learn(state, state2, reward, action)

Answer 1

I guess it could be because the environment is stochastic. Learning the model in stochastic environment may lead to sub-optimal policies. In the Sutton & Barto's RLBook they say that they assume deterministic environment.

Answer 2

Check that after a model step is taken the planning steps sample from the next state ie state2 .

If not, planning might be taking repeated steps from the same starting state given by self.env .

However, I may have misunderstood the role of the self.env parameter in self.model.sample(self.env)