پروژه پایتون / پایتورچ / 69588

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شرح پروژه

سلام وقت تون بخیر
یه پروژه دارم میخواهم برام انجام بدین لطفا
پروژه ام DDPG Algorithm هست که با پایتون و پایتورج کار میشه خواهش میکنم برسی کنین بهم خبر بدین
امروز دوشنبه هست تا جمعه وقت داره
DDPG Algorithm implementation on PyTorch

پروژه ام الگوریتم DDPG هست و با پایتورج کار میشه تو محیط پایتون

DDPG Algorithm implementation on PyTorch belong to reinforcement learning

پروژه ام مربوط یادگیری تقویتی میشه

Deep Deterministic Policy Gradient (DDPG) is an actor-critic reinforcement learning algorithm used for continuous action spaces. It combines ideas from deep Q-learning and policy gradients.

- Actor-Critic: DDPG maintains both an actor network (policy) and a critic network (value function). The actor suggests actions, while the critic evaluates those actions.

- Deterministic Policy: Unlike stochastic policies, DDPG learns a deterministic policy, mapping states to specific actions. This simplifies the learning process.

- Experience Replay: DDPG employs experience replay, storing past experiences in a replay buffer and randomly sampling batches for training. This helps break temporal correlations in the data.

- Target Networks: To stabilize training, DDPG uses target networks for both the actor and critic. These slowly track the learned networks over time.

- Q-Value Function: The critic estimates the Q-value of the current state-action pair, guiding the actor towards actions that maximize expected rewards.

- Exploration: DDPG adds noise to the selected actions during exploration to strike a balance between exploration and exploitation.

The algorithm is effective for tasks with continuous action spaces, such as robotic control or certain types of games.

DDPG, or Deep Deterministic Policy Gradient, is a reinforcement learning algorithm designed for solving problems with continuous action spaces

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