ankitsharma-tech/Deep-Reinforcement-Learning-With-Pytorch
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3.
This project provides pre-built, clear implementations of various deep reinforcement learning algorithms like DQN, PPO, and SAC. It takes a simulated environment's observations and rewards as input and outputs trained models that can make optimal decisions or perform specific actions within that environment. This is for researchers and practitioners who are experimenting with, comparing, or developing new reinforcement learning agents for simulated control tasks.
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Use this if you need a collection of established deep reinforcement learning algorithms implemented in PyTorch, ready for use in environments like OpenAI Gym.
Not ideal if you are a beginner looking for a high-level API to quickly apply RL without understanding the underlying algorithms, or if you need robust, production-ready deployments.
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Python
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MIT
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Last pushed
Sep 24, 2025
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