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Found 12 papers, 11 papers with code
The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization
This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work.
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
Zephyr: Direct Distillation of LM Alignment
Starting from a dataset of outputs ranked by a teacher model, we apply distilled direct preference optimization (dDPO) to learn a chat model with significantly improved intent alignment.
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time.
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
EnvPool is open-sourced at https://github. com/sail-sg/envpool.
A2C is a special case of PPO
Advantage Actor-critic (A2C) and Proximal Policy Optimization (PPO) are popular deep reinforcement learning algorithms used for game AI in recent years.
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning algorithms.
Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning
In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agents that could defeat professional players in StarCraft~II.
Griddly: A platform for AI research in games
In recent years, there have been immense breakthroughs in Game AI research, particularly with Reinforcement Learning (RL).
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games
Training agents using Reinforcement Learning in games with sparse rewards is a challenging problem, since large amounts of exploration are required to retrieve even the first reward.
A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved state-of-the-art performance in many challenging strategy games.
Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS
This paper presents a preliminary study comparing different observation and action space representations for Deep Reinforcement Learning (DRL) in the context of Real-time Strategy (RTS) games.
