PyTorch implementation of Soft Actor-Critic (SAC)
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Updated
Dec 5, 2021 - Jupyter Notebook
PyTorch implementation of Soft Actor-Critic (SAC)
A Modular Library for Off-Policy Reinforcement Learning with a focus on SafeRL and distributed computing
PyTorch implementation of D4PG with the SOTA IQN Critic instead of C51. Implementation includes also the extensions Munchausen RL and D2RL which can be added to D4PG to improve its performance.
A collection of algorithms for Deep Reinforcement Learning (DRL). Algorithms covered include Value-Based, Policy-Based and Actor-Critic Methods.
A2C and D4PG implementations for the continuous control challenge. Part of the coursework for Udacity's Deep RL Nanodegree.
Distributed PyTorch implementation of D4PG with ray. Using a SOTA IQN Critic instead of C51. Implementation includes also the extensions Munchausen RL and D2RL which can be added to D4PG to improve its performance.
Multi-agent reinforcement learning where an agent learns to play tennis against itself. Part of the coursework for Udacity's Deep RL Nanodegree.
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