PyTorch implementation of "Sample-efficient Imitation Learning via Generative Adversarial Nets"
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Updated
Aug 9, 2021 - Python
PyTorch implementation of "Sample-efficient Imitation Learning via Generative Adversarial Nets"
A RL agent that learns to play doom's deadly corridor based on DDQN and PER.
PyTorch implementation of our work: "Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning"
TensorFlow implementation of "Sample-efficient Imitation Learning via Generative Adversarial Nets"
PROJECT MIGRATED TO CODEBERG - Reinforcement Learning in Multiplicative Domains
PyTorch implementation of our work: "Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learning"
PyTorch implementation of our work: "Where is the Grass Greener? Revisiting Generalized Policy Iteration for Offline Reinforcement Learning"
My content of CS294 Deep Reinforcement Learning course, conduced by Sergey Levine from UC Berkeley.
Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms
An Optimistic Approach to the Q-Network Error in Actor-Critic Methods
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning
Containing a custom-built Reinforcement Learning environment and implementations of key RL algorithms like Q-learning and SARSA, tested in scenarios such as a drone navigation challenge and the Frozen Lake environment.
Temporal Difference Method - Q-Learning Implementation for FrozenLake Grid Problem
off-policy algorithm utilizing offline and online data
Contains PyTorch Implementation of the following off policy actor critic algorithms
Stochastic Weighted Twin Delayed Deep Deterministic Policy Gradient (SWTD3)
Sample Policy Gradient
PyTorch-implementation-DICE-algorithms
Repository containing basic algorithm applied in python.
A novel method to incorporate existing policy (Rule-based control) with Reinforcement Learning.
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