PPO agent is not learning
I’m trying to train the model that selects the maximum number in the list of 10 numbers.
PPO implementation in Pytorch: gradients calculation
I ma trying to implement Proximal Policy Optimization(PPO) in pytorch and apply it to BipedalWalker-v3 gym environment.
PPO implementation in Pytorch: gradient calculation
I ma trying to implement Proximal Policy Optimization(PPO) in pytorch and apply it to BipedalWalker-v3 gym environment.
Seeking Insights: Enhancing a Connect Four AI Project
I’ve been working on a Connect Four AI project, developed primarily as a learning opportunity to explore reinforcement learning (RL) in a two-agent gameplay environment. The project is structured around training and testing AI models to master Connect Four, using a Python-based setup.
Can Kullback–Leibler divergence KL(P||Q) = KL(Q||P) > 0? RL algorithm implementation
I am implementing the spatial smoothness framework in my RL algorithm (paper). In this framework, I added a noise to the current state (observation) and then calculated the distribution from the noisy state. I intend to calculate Jeffrey’s divergence (symmetric KL-divergence). However, I have encountered a situation where KL(P||Q) = KL(Q||P), and both are greater than zero. Is this possible? My code is as follows:
Can Kullback–Leibler divergence KL(P||Q) = KL(Q||P) > 0? RL algorithm implementation
I am implementing the spatial smoothness framework in my RL algorithm (paper). In this framework, I added a noise to the current state (observation) and then calculated the distribution from the noisy state. I intend to calculate Jeffrey’s divergence (symmetric KL-divergence). However, I have encountered a situation where KL(P||Q) = KL(Q||P), and both are greater than zero. Is this possible? My code is as follows:
Can KL(P||Q) = KL(Q||P) > 0? RL algorithm implementation
I am implementing the spatial smoothness framework in my RL algorithm (paper). In this framework, I added a noise to the current state (observation) and then calculated the distribution from the noisy state. I intend to calculate Jeffrey’s divergence (symmetric KL-divergence). However, I have encountered a situation where KL(P||Q) = KL(Q||P), and both are greater than zero. Is this possible? My code is as follows: