A Torch Based RL Framework for Rapid Prototyping of Research Papers
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Updated
Oct 29, 2024 - Python
A Torch Based RL Framework for Rapid Prototyping of Research Papers
Modularized Implementation of Deep RL Algorithms in PyTorch
Deep Reinforcement Learning: Value-Based methods. An implementation of DQN, DDQN, Dueling Architectures, DQV, DQV-Max on the PyTorch Lightning framework.
Example Dueling DQN implementation with ReLAx
RL based agent for atari games
Reinforcement learning agent using dqqn, dueling network, per to play the google chrome trex browser game.
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.
Open AI gym lunar-lander solution using Deep Q-Learning Network Architectures
A RL agent that learns to play doom's deadly corridor based on DDQN and PER.
Deep reinforcement learning agent
Using the DRL algorithms put forward by Deepmind to play Atari 2600 Games with a comparison of algorithm performance
Reinforcement Learning Playground
TensorFlow implementation of Deep RL (Reinforcement Learning) papers based on deep Q-learning (DQN)
DQN, Double DQN, Dueling Network
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