RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code
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Updated
May 12, 2024 - Python
RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code
Multi-hop knowledge graph reasoning learned via policy gradient with reward shaping and action dropout
Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO
This repo implements our paper, "Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt", which has been accepted at NeurIPS 2023.
Guide Your Agent with Adaptive Multimodal Rewards (NeurIPS 2023 Accepted)
TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets
Dota 2 bot that is trained by Deep RL with expert demonstrations
Code for NeurIPS 2022 paper Exploiting Reward Shifting in Value-Based Deep RL
Bayesian Reward Shaping Framework for Deep Reinforcement Learning
3D gym environments to train RL agents to play the Slime Volleyball game in 3 dimensions using Webots as simulator.
Code for "DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks"
Set of experiments of using weights of a previously trained network as prior knowledge for a more complicated one and reward providing.
Reward shaping library
Benchmarks for risk-aware reward shaping of autonomous driving
A lightweight package for running small experiments with reward shaping in reinforcement learning.
Reinforcement Learning Exploration of PPO and training methods in Rocket League
This repo demonstrates basic Q-learning for the Mountain Car Gym environment. It also shows how reward shaping can result in faster training of the agent.
Prepares policies from data to model; focuses on hierarchical tasks and applies reward shaping to handle delayed reward signals.
Code from the IJCAI 2019 paper "Controllable Neural Story Plot Generation via Reward Shaping"
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