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# Reinforcement Learning

## Changes from 2071c852751535b6df1d28671a9dbb31a4fb600d to 4aade5fa86615f3fb780245310da53a93b0ac39f

--- title: Reinformcement Learning Part 1 categories: session --- **To be completed** + [Reinforcement Learning Slides 2.pdf]() + **Goal** Understand and be able to implement Q-learning + **Reading** Russel and Norvig Chapter 23 + [Eirik's slides from 2022](Reinforcement Learning Slides 2.pdf) # Exercises ## Task 1 Last week we implemented the Q-Function: Last session we implemented the Q-Function: $$Q(s,a) = \sum_{s'}P(s'|s,a)[R(s,a,s') + \gamma \max_{a'}Q(s',a')]$$ and the function for the optimal policy based on the results from the Q-Function: $\pi^*(s) = \mathop{\text{argmax }}\limits_aQ(s,a)$ We also had an assignment where we moved based on a list of calculated utilities. In this assignment we will be implementing table based Q-learning, a model-free, off-policy reinforcement learning algorithm. For now, we want to just try to play using a list of calculated q-values instead of the utilities, you can use the following q-values: ```python def create_filled_q_table() -> np.array: """Creates a 'filled' q-table for the default frozen-lake environment. Returns: Filled q-table """ return np.array([ [0.009, 0.192, 0.007, 0.009], [0.003, 0.002, 0.003, 0.17], [0.003, 0.002, 0.001, 0.067], [0.001, 0.001, 0.002, 0.037], [0.526, 0.002, 0.001, 0.002], [0., 0., 0., 0.], [0.046, 0., 0., 0.], [0., 0., 0., 0.], [0.002, 0.002, 0.002, 0.709], [0.001, 0.597, 0.001, 0.001], [0.945, 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0.02, 0.012, 0.898, 0.016], [0.061, 0.991, 0.092, 0.068], [0., 0., 0., 0.] ]) ``` ### Part A We will use the latter in this task, so if you did not already, ***make a python implementation of this policy***. You can use the following 'skeleton': ```python def optimal_policy(q_sa: np.array, state: int) -> int: """RL-policy for optimal play. Args: q_sa: q-table state: current state Returns: optimal action for current state and q-table. """ ... ``` ### Part B Below I have implemented two functions ```play``` and ```test_performance```. ```python import numpy as np import gym from collections.abc import Callable def play(env: gym.Env, q_sa: np.array, policy: Callable, m_ep_length: int = 100, render: bool = True) -> None: """Plays one episode of environment env using an optimal policy from the q-table q_sa. Args: env: Gym environment q_sa: q-table policy: the policy function to play with m_ep_length: max episode length, default; 100, should be set higher for large environments render: render environment ? Returns: None """ state, info = env.reset(return_info=True) if render: env.render() j = 0 while j < m_ep_length: j += 1 action = policy(q_sa, state) # TODO: Implement this function first! new_state, reward, done, info = env.step(action) state = new_state if render: env.render() if done: break def test_performance(env: gym.Env, q_sa: np.array, policy: Callable, n_episodes: int = 1000, m_ep_length: int = 100) -> float: """ Args: env: Gym environment q_sa: q-table policy: the policy function to play with n_episodes: number of episodes m_ep_length: max episode length Returns: average reward """ rewards = 0 for i in range(n_episodes): state, info = env.reset(return_info=True) reward = 0 j = 0 while j < m_ep_length: j += 1 action = policy(q_sa, state) # TODO: Implement new_state, reward, done, info = env.step(action) state = new_state if done: break rewards += reward return rewards / n_episodes if __name__ == "__main__": environment = gym.make('FrozenLake-v1', desc=None, map_name="4x4", is_slippery=True) q_table = create_filled_q_table() # TODO: Implement play(environment, q_table, optimal_policy) # TODO: Implement policy ``` Using either this implementation, or your own: - ***Using your optimal policy from part 1, play at least one episode*** -- Is it playing "optimally"? - ***Calculate the performance of the policy using the ```test_performance``` function.*** -- What does the number here mean? ## Task 2 In this task we will create functions to update our own q-table, for now you can turn make the environment deterministic by turning of the 'slippery' argument when making the environment: ```python environment = gym.make('FrozenLake-v1', desc=None, map_name="4x4", is_slippery=False) ``` ### Part A First we need to create an empty q-table, as the gym framework supports frozen-lake environment of different sizes, we need to initialize it with the size "state_space x action_space". You can get them from: ```python n_states = env.observation_space.n # state-space n_actions = env.action_space.n ``` **Create a function to initialize a q-table** You can use the following 'skeleton': ```python def initialize_q_table(env: gym.Env) -> np.array: """Creates and returns an empty q-table of size state_space x action_space. Args: env: Gym environment Returns: np.array of q-table of size state_space x action_space """ ... ``` ### Part B Now we need some way to update our q-table. Recall the Q Temporal-Difference function to update the q-values: $Q(s,a) \leftarrow Q(s,a) + \alpha[R(s,a,s') + \gamma \max\limits_{a'}Q(s',a') - Q(s,a)]$ **Implement a function to calculate the value to be updated (the part on the right side of the arrow)** You can use the following 'skeleton': ```python def q_temporal_difference(q_sa: np.array, action: int, reward: float, start_state: int, end_state: int, alpha: float = 0.85, gamma: float = 0.98) -> float: """Calculates the q-update. Args: q_sa: q-table action: action we are taking reward: result of R(s,a,s') start_state: start state end_state: end state (after taking action a) alpha: learning-rate gamma: discount Returns: q-td update value """ ... ``` ### Part C Using the functions we implemented above, we want to update the q-table by simply having the agent play the game a lot. ***Implement a q-learning function***, you can use the following 'skeleton' and/or take inspiration from the ```test_performance``` implementation. ```python def q_learning(env: gym.Env, policy: Callable, n_episodes: int = 10000, m_ep_length: int = 100) -> np.array: """q-learning implementation to update a q-table. Args: env: gym environment policy: policy function n_episodes: number of episodes to train on m_ep_length: maximum episode length Returns: updated q-table """ ... ``` Note that your agent might behave odd (or not work at all), if you use your optimal policy on an empty q-table, so you may want to edit it to take a random action if it has issues differentiating between actions. ### Part D ***Try out your algorithm***; - Use the updated q-table with the ```play``` function and the ```test_performance``` function. - Print out the q-table Are there any potential problems? What if you train it again with ```slippery=True``` ? ## Task 3 (From now on, we will play with a stochastic environment, set ```slippery=True```). We need some way to encourage exploration, to prevent the agent from only trying to repeat the first sequence that got him to the goal. There are multiple ways to implement this; 1. We can set a static epsilon $\epsilon$ value, and set the action to some random action a if some random number n is below $\epsilon$. 2. We usually want to encourage exploration in earlier training phases, and encourage exploitation in the later ones. We can therefore use a similar approach to 1, but with the addition of decaying $\epsilon$ over time. 3. The third option (non-exhaustive) is to create a policy that picks an action based on a weighted probability-distribution created based on the q-values. The weighting can then change over time to encourage exploration early, and exploitation later. A modified version of this function could also be used when 'playing' the game, if you want a policy that not necessarily always picks the option with the highest utility. ### Part A ***Implement \_one\_ of the functions above***, you can use the following 'skeleton': ```python def epsilon_policy(q_sa: np.array, state: int, env: gym.Env, eps: float = 0.2) -> int: """RL epsilon-greedy policy. Policy for exploration/exploitation tradeoff. Args: q_sa: q-table state: current state env: gym environment eps: epsilon Returns: action with a 1-eps chance of being exploitation, eps chance of being exploration """ ... ``` ### Part B ***Try out your algorithm***; - Use the updated q-table with the ```play``` function and the ```test_performance``` function. - Print out the q-table ## Task 4 ### Part A ***Modify your q-learning algorithm to call ```test_performance``` every n-episode. Save this in a table and plot the result using matplotlib.*** We now have a way to calculate the performance over time/training episodes. ### Part B ***Experiment with the different hyperparameters (epsilon, learning-rate, gamma, etc) and compare them using the method in part A.*** You can also try with other versions of the frozen-lake environment (e.g. the 8x8 map), they have a function to create random maps. ## Task 5 (Extra) Please inform me if you get to this point early, as I might change the task, but for now: ***Implement SARSA, and repeat similar experiments from task 4 to compare the two.***