Is reward accumulated during a play iteration when performing SARSA?2019 Community Moderator ElectionQ-learning with a state-action-state reward structure and a Q-matrix with states as rows and actions as columnsDefining State Representation in Deep Q-LearningWhy is my loss function for DQN converging too quickly?Reinforcement learning: decreasing loss without increasing rewardWhat is the immediate reward in value iteration?Reinforcement learning: negative reward (punish) illegal actions?Is every value-iteration off-policy DP is a Q-learning?Is DQN a SMART (Semi Markov Average Reward Technique)?how to choose between discounted reward and average rewardReward function to avoid illegal actions, minimize legal action and learn to win - Reinforcement Learning
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Is reward accumulated during a play iteration when performing SARSA?
2019 Community Moderator ElectionQ-learning with a state-action-state reward structure and a Q-matrix with states as rows and actions as columnsDefining State Representation in Deep Q-LearningWhy is my loss function for DQN converging too quickly?Reinforcement learning: decreasing loss without increasing rewardWhat is the immediate reward in value iteration?Reinforcement learning: negative reward (punish) illegal actions?Is every value-iteration off-policy DP is a Q-learning?Is DQN a SMART (Semi Markov Average Reward Technique)?how to choose between discounted reward and average rewardReward function to avoid illegal actions, minimize legal action and learn to win - Reinforcement Learning
$begingroup$
I've been having issue with getting my DQN to converge to a good solution for snake. Regardless of the different types of reward functions I've tried, it seems that the snake is indefinitely going around in circles. I have not tried exploring more states yet because I am confused about how to properly assign reward.
Currently, I am using a 2D-Gaussian distribution to assign reward where $f(x=x_food,y=y_food) = 1$. Terminal states like hitting the wall or itself result in a reward value of -1.
My reason for using the Gaussian was because of the relatively sparse rewards in this game and the ability to easily clip rewards between [1,-1] in meaning full way.
I have two questions.
- Is this an appropriate way to define the reward function?
- Currently during training, I do no accumulate the reward during each play iteration. So each reward for transitions are independent of the reward values before it. Right now I am doing $big[S_1,A_1,R_1,S_2,A_2,R_2,S_3big]$. I've looked at other code where people have accumulate the reward like $big[S_1,A_1,R_1,S_2,A_2,R_2 = R_1+r,S_3big]$. Where $r$ is given by the reward function. The thing is, I can't find a paper that defines why you should do this. So my question is, which way is the appropriate way to assign reward?
machine-learning deep-learning reinforcement-learning q-learning dqn
asked Mar 29 at 1:44
DevarakondaVDevarakondaV
164
$endgroup$
add a comment |
$begingroup$
I've been having issue with getting my DQN to converge to a good solution for snake. Regardless of the different types of reward functions I've tried, it seems that the snake is indefinitely going around in circles. I have not tried exploring more states yet because I am confused about how to properly assign reward.
Currently, I am using a 2D-Gaussian distribution to assign reward where $f(x=x_food,y=y_food) = 1$. Terminal states like hitting the wall or itself result in a reward value of -1.
My reason for using the Gaussian was because of the relatively sparse rewards in this game and the ability to easily clip rewards between [1,-1] in meaning full way.
I have two questions.
- Is this an appropriate way to define the reward function?
- Currently during training, I do no accumulate the reward during each play iteration. So each reward for transitions are independent of the reward values before it. Right now I am doing $big[S_1,A_1,R_1,S_2,A_2,R_2,S_3big]$. I've looked at other code where people have accumulate the reward like $big[S_1,A_1,R_1,S_2,A_2,R_2 = R_1+r,S_3big]$. Where $r$ is given by the reward function. The thing is, I can't find a paper that defines why you should do this. So my question is, which way is the appropriate way to assign reward?
machine-learning deep-learning reinforcement-learning q-learning dqn
asked Mar 29 at 1:44
DevarakondaVDevarakondaV
164
$endgroup$
add a comment |
$begingroup$
I've been having issue with getting my DQN to converge to a good solution for snake. Regardless of the different types of reward functions I've tried, it seems that the snake is indefinitely going around in circles. I have not tried exploring more states yet because I am confused about how to properly assign reward.
Currently, I am using a 2D-Gaussian distribution to assign reward where $f(x=x_food,y=y_food) = 1$. Terminal states like hitting the wall or itself result in a reward value of -1.
My reason for using the Gaussian was because of the relatively sparse rewards in this game and the ability to easily clip rewards between [1,-1] in meaning full way.
I have two questions.
- Is this an appropriate way to define the reward function?
- Currently during training, I do no accumulate the reward during each play iteration. So each reward for transitions are independent of the reward values before it. Right now I am doing $big[S_1,A_1,R_1,S_2,A_2,R_2,S_3big]$. I've looked at other code where people have accumulate the reward like $big[S_1,A_1,R_1,S_2,A_2,R_2 = R_1+r,S_3big]$. Where $r$ is given by the reward function. The thing is, I can't find a paper that defines why you should do this. So my question is, which way is the appropriate way to assign reward?
machine-learning deep-learning reinforcement-learning q-learning dqn
asked Mar 29 at 1:44
DevarakondaVDevarakondaV
164
$endgroup$
I've been having issue with getting my DQN to converge to a good solution for snake. Regardless of the different types of reward functions I've tried, it seems that the snake is indefinitely going around in circles. I have not tried exploring more states yet because I am confused about how to properly assign reward.
Currently, I am using a 2D-Gaussian distribution to assign reward where $f(x=x_food,y=y_food) = 1$. Terminal states like hitting the wall or itself result in a reward value of -1.
My reason for using the Gaussian was because of the relatively sparse rewards in this game and the ability to easily clip rewards between [1,-1] in meaning full way.
I have two questions.
- Is this an appropriate way to define the reward function?
- Currently during training, I do no accumulate the reward during each play iteration. So each reward for transitions are independent of the reward values before it. Right now I am doing $big[S_1,A_1,R_1,S_2,A_2,R_2,S_3big]$. I've looked at other code where people have accumulate the reward like $big[S_1,A_1,R_1,S_2,A_2,R_2 = R_1+r,S_3big]$. Where $r$ is given by the reward function. The thing is, I can't find a paper that defines why you should do this. So my question is, which way is the appropriate way to assign reward?
machine-learning deep-learning reinforcement-learning q-learning dqn
machine-learning deep-learning reinforcement-learning q-learning dqn
asked Mar 29 at 1:44
DevarakondaVDevarakondaV
164
asked Mar 29 at 1:44
DevarakondaVDevarakondaV
164
asked Mar 29 at 1:44
DevarakondaVDevarakondaV
164
asked Mar 29 at 1:44
DevarakondaVDevarakondaV
164
asked Mar 29 at 1:44
DevarakondaVDevarakondaV
164
164
add a comment |
add a comment |
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