Modularized Implementation of Deep RL Algorithms in PyTorch
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Updated
Apr 16, 2024 - Python
Modularized Implementation of Deep RL Algorithms in PyTorch
PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF) and Extensions: N-step Bootstrapping, PER, Noisy Layer, Dueling Networks, and parallelization.
TensorFlow implementation of Deep RL (Reinforcement Learning) papers based on deep Q-learning (DQN)
A Torch Based RL Framework for Rapid Prototyping of Research Papers
Deep reinforcement learning agent
Reinforcement Learning Playground
RL based agent for atari games
Using the DRL algorithms put forward by Deepmind to play Atari 2600 Games with a comparison of algorithm performance
Reinforcement learning agent using dqqn, dueling network, per to play the google chrome trex browser game.
DQN, Double DQN, Dueling Network
A RL agent that learns to play doom's deadly corridor based on DDQN and PER.
Example Dueling DQN implementation with ReLAx
Deep Reinforcement Learning: Value-Based methods. An implementation of DQN, DDQN, Dueling Architectures, DQV, DQV-Max on the PyTorch Lightning framework.
Open AI gym lunar-lander solution using Deep Q-Learning Network Architectures
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