What will be the policy if the state space is continuous in Reinforcement learning
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I have started recently with reinforcement learning. I have few doubts regarding the policy of an agent when it comes to continuous space. From my understanding, policy tells the agent which action to perform given a particular state. This makes sense when it comes to the maze example, where the state space is descrete and limited. What if the state space is continuous, will the agent have information of every possible state in the state space?
Also will an RL agent be able to take decision if its in a new state that it has not encountered during training ?
reinforcement-learning
asked 2 mins ago
ChinniChinni
205
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add a comment |
$begingroup$
I have started recently with reinforcement learning. I have few doubts regarding the policy of an agent when it comes to continuous space. From my understanding, policy tells the agent which action to perform given a particular state. This makes sense when it comes to the maze example, where the state space is descrete and limited. What if the state space is continuous, will the agent have information of every possible state in the state space?
Also will an RL agent be able to take decision if its in a new state that it has not encountered during training ?
reinforcement-learning
asked 2 mins ago
ChinniChinni
205
$endgroup$
add a comment |
$begingroup$
I have started recently with reinforcement learning. I have few doubts regarding the policy of an agent when it comes to continuous space. From my understanding, policy tells the agent which action to perform given a particular state. This makes sense when it comes to the maze example, where the state space is descrete and limited. What if the state space is continuous, will the agent have information of every possible state in the state space?
Also will an RL agent be able to take decision if its in a new state that it has not encountered during training ?
reinforcement-learning
asked 2 mins ago
ChinniChinni
205
$endgroup$
I have started recently with reinforcement learning. I have few doubts regarding the policy of an agent when it comes to continuous space. From my understanding, policy tells the agent which action to perform given a particular state. This makes sense when it comes to the maze example, where the state space is descrete and limited. What if the state space is continuous, will the agent have information of every possible state in the state space?
Also will an RL agent be able to take decision if its in a new state that it has not encountered during training ?
reinforcement-learning
reinforcement-learning
asked 2 mins ago
ChinniChinni
205
asked 2 mins ago
ChinniChinni
205
asked 2 mins ago
ChinniChinni
205
asked 2 mins ago
ChinniChinni
205
asked 2 mins ago
ChinniChinni
205
205
add a comment |
add a comment |
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