پروژه یادگیری تقویتی با پایتون و پایتورچ(7409)

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DDPG Algorithm implementation on PyTorch

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

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DDPG Algorithm implementation on PyTorch environment

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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.

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