PPO Algorithm
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Description:
PPO (Proximal Policy Optimization) is a popular reinforcement learning algorithm that uses trust region methods to update the policy. The goal is to train an agent to make decisions in a complex environment.
Constraints:
Example:
Suppose we have a simple grid world where the agent can move up, down, left, or right. The reward is 1 for reaching the goal state and -1 for hitting a wall.
Test Cases
Test Case 1
Input:
[[1,2,3],[4,5,6]]Expected:
[[0.5,0.3,0.2],[0.7,0.2,0.1]]Test Case 2
Input:
[[7,8,9],[10,11,12]]Expected:
[[0.6,0.4,0.0],[0.8,0.1,0.1]]+ 3 hidden test cases