Answers.Body: <blockquote>
  <p>I would like to know if im right and if there is method to solve that or if TD method are just useless in this case ?</p>
  </blockquote>
  <p>Your assessment is partially correct, but TD learning algorithms have a behaviour which helps with MountainCar and other situations where there is a meaningful reward structure that the algorithm can learn from.</p>
  <p>Specifically in MountainCar, there is a negative reward per time step, because the goal is to end the episode efficiently and quickly. The TD algorithm is updated on every time step, and the <span class="math-container">$R$</span> value in the update will change current estimates. This means that actions that lead back to already-experienced states will predict lower action values than randomly initialised ones (on average), and this provides data that the agent can use to explore new states and actions. As long as you don't run the first episode with <span class="math-container">$\epsilon=1.0$</span> all the way to the end, the agent will stop behaving randomly and will start to explore further away from the start states.</p>
  <p>In MountainCar, you can see this effect as during the first few episodes the car will tend to gain kinetic energy at the bottom of the valley, as this is a new unexplored state. You can see similar behaviour in maze-solving TD-learning agents which will tend to explore new branches whilst learning, instead of performing a fully random walk.</p>
  <p>You cannot always arrange for this effect. But Reinforcement Learning will often work better if there is some basic reward signal that encourages exploring state and action space. Some algorithms have this built in - the UCB action choice, and DynaQ+ bonus reward both encourage agents to take novel actions and explore the state/action space more methodically than at random.</p>
  <p>This need to explore can even be turned into an &quot;intrinsic reward&quot;, making crude models of curiosity and boredom than can motivate an agent to search complex spaces and discover solutions with better success rates than simple random action choice.</p>
  <p>Not all RL scenarios require this behaviour, and some may not benefit from it. If there is enough reward structure already present, or it can be added by a developer without adding opinions about how the agent solves a problem, then the agent should find good trajectories. However, some optimisation problems where a goal state is effectively hidden except by random guessing, are not easily solvable using RL. Decrypting a text that was encrypted by modern encryption techniques for example gives no clues as to when a guess at a secret key is &quot;close&quot; - just one bit out and the impact looks completely random. An RL agent whose action is to guess a secret key to decrypt a message would be a waste of time.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-07-08T16:00:46.693Z
Answers.Text: Thank you for your complete answer !! That was very instructive :) 
  I block on one point : you said that in theory if we let for example our moutaincar agent a long time, he will try to explore unexplore state because the state that he explored have a the worst state value and it seems right, but when I tried it its looks like my agent never find the goal even with 10 000 episodes, and a updating every step. Looks weird because with so much time, he should in theory explore all the state and so the goal. Ty for reading :)
Answers.UserDisplayName: Cauchy_Chlasse
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Answers.CreationDate: 2024-07-08T16:16:18.113Z
Answers.Text: @Cauchy_Chlasse I have used MountainCar with some different agent types, including SARSA and Q-learning. It always finds the goal in my experience, although it can take a very long time in the first episode e.g. 5000 steps, before it starts to learn to do it efficiently. You may want to check that you have not set epsilon to 1 without any chance to get lower during the episode or other similar issue.
Answers.UserDisplayName: Neil Slater
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Answers.CreationDate: 2024-07-08T17:18:16.517Z
Answers.Text: Im using DeepQ learning algorithm with constant epsilon = 0.1 to make sure to exploit the most of the time and to try to observe your point. Do you think because I have a low exploration rate it will make MORE time to find the solution but the agent will still find it ?
Answers.UserDisplayName: Cauchy_Chlasse
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-07-08T17:19:26.100Z
Answers.Text: I think that should be fine. I don't know what could be going wrong for you
Answers.UserDisplayName: Neil Slater
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-07-08T17:44:26.853Z
Answers.Text: Okaaaay I will try to debug that, thank your for your help and your time man :) Have a good day !!
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Answers.UserDisplayName: Neil Slater
Body: <p>Im trying to solve MoutainCar and CarRacing (i love cars) in gym environnement with DDPG and my algo struggle.</p>
  <p>I have think on why this don't work and I would like to know if my resoning is false.</p>
  <p>When we deal with type of environnement where 1 step is a negative reward and we use TD method to update the Critic, the estimation of the Critic will be always false because we update it like that :
  <span class="math-container">$$Q(state) \rightarrow target=R + \gamma Q(next\_state) \\\\$$</span></p>
  <p>The problem is Q(next_state) is just a random value give by our network during weight initialization so it's a false value until we find the goal and our target=R.From this point the Critic Network will learn stable value and we will have a sort of backpropagation from this state.
  That's why I think that if we don't find  the goal randomly we are not able to solve this type of env with algo like DDPG. I would like to know if im right and if there is method to solve that or if TD method are just useless in this case ?</p>
  <p>I can imagine for simple environnement like moutain car wwe can find the goal with random action and then learn but how we deal with more complicated env like racingcar where its almost impossible to find goal randomly ?</p>
  <p>Ty for reading !! :)</p>
  
Tags: <reinforcement-learning><deep-rl><actor-critic-methods><ddpg>
Title: Actor Critic need to find the goal to have good update and suceed?
UserDisplayName: Cauchy_Chlasse
ThreadText: # Actor Critic need to find the goal to have good update and suceed?
  
  (Asked by: username_0 on 2024-07-07)
  
  Im trying to solve MoutainCar and CarRacing (i love cars) in gym environnement with DDPG and my algo struggle.
  
  I have think on why this don't work and I would like to know if my resoning is false.
  
  When we deal with type of environnement where 1 step is a negative reward and we use TD method to update the Critic, the estimation of the Critic will be always false because we update it like that :
  $$Q(state) \rightarrow target=R + \gamma Q(next\\_state) \\\\$$
  
  The problem is Q(next\_state) is just a random value give by our network during weight initialization so it's a false value until we find the goal and our target=R.From this point the Critic Network will learn stable value and we will have a sort of backpropagation from this state.
  That's why I think that if we don't find the goal randomly we are not able to solve this type of env with algo like DDPG. I would like to know if im right and if there is method to solve that or if TD method are just useless in this case ?
  
  I can imagine for simple environnement like moutain car wwe can find the goal with random action and then learn but how we deal with more complicated env like racingcar where its almost impossible to find goal randomly ?
  
  Ty for reading !! :)
  
  **Tags:** reinforcement-learning, deep-rl, actor-critic-methods, ddpg
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-07-07. Score: 1 | Accepted answer
  
  > I would like to know if im right and if there is method to solve that or if TD method are just useless in this case ?
  
  Your assessment is partially correct, but TD learning algorithms have a behaviour which helps with MountainCar and other situations where there is a meaningful reward structure that the algorithm can learn from.
  
  Specifically in MountainCar, there is a negative reward per time step, because the goal is to end the episode efficiently and quickly. The TD algorithm is updated on every time step, and the $R$ value in the update will change current estimates. This means that actions that lead back to already-experienced states will predict lower action values than randomly initialised ones (on average), and this provides data that the agent can use to explore new states and actions. As long as you don't run the first episode with $\epsilon=1.0$ all the way to the end, the agent will stop behaving randomly and will start to explore further away from the start states.
  
  In MountainCar, you can see this effect as during the first few episodes the car will tend to gain kinetic energy at the bottom of the valley, as this is a new unexplored state. You can see similar behaviour in maze-solving TD-learning agents which will tend to explore new branches whilst learning, instead of performing a fully random walk.
  
  You cannot always arrange for this effect. But Reinforcement Learning will often work better if there is some basic reward signal that encourages exploring state and action space. Some algorithms have this built in - the UCB action choice, and DynaQ+ bonus reward both encourage agents to take novel actions and explore the state/action space more methodically than at random.
  
  This need to explore can even be turned into an "intrinsic reward", making crude models of curiosity and boredom than can motivate an agent to search complex spaces and discover solutions with better success rates than simple random action choice.
  
  Not all RL scenarios require this behaviour, and some may not benefit from it. If there is enough reward structure already present, or it can be added by a developer without adding opinions about how the agent solves a problem, then the agent should find good trajectories. However, some optimisation problems where a goal state is effectively hidden except by random guessing, are not easily solvable using RL. Decrypting a text that was encrypted by modern encryption techniques for example gives no clues as to when a guess at a secret key is "close" - just one bit out and the impact looks completely random. An RL agent whose action is to guess a secret key to decrypt a message would be a waste of time.
  
  > **Comments:**
  > **username_0**: Thank you for your complete answer !! That was very instructive :)  I block on one point : you said that in theory if we let for example our moutaincar agent a long time, he will try to explore unexplore state because the state that he explored have a the worst state value and it seems right, but when I tried it its looks like my agent never find the goal even with 10 000 episodes, and a updating every step. Looks weird because with so much time, he should in theory explore all the state and so the goal. Ty for reading :)  
  > **username_1**: @username_0 I have used MountainCar with some different agent types, including SARSA and Q-learning. It always finds the goal in my experience, although it can take a very long time in the first episode e.g. 5000 steps, before it starts to learn to do it efficiently. You may want to check that you have not set epsilon to 1 without any chance to get lower during the episode or other similar issue.  
  > **username_0**: Im using DeepQ learning algorithm with constant epsilon = 0.1 to make sure to exploit the most of the time and to try to observe your point. Do you think because I have a low exploration rate it will make MORE time to find the solution but the agent will still find it ?  
  > **username_1**: I think that should be fine. I don't know what could be going wrong for you  
  > **username_0**: Okaaaay I will try to debug that, thank your for your help and your time man :) Have a good day !!  
  
  ---
  
  