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Simple and easily configurable grid world environments for reinforcement learning
A collection of reference environments for offline reinforcement learning
FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow
MiniHack is a sandbox framework for easily designing rich and diverse environments for Reinforcement Learning (RL). Based on the game of NetHack, MiniHack uses the NetHack Learning Environment (NLE) to communicate with the game and to provide a convenient interface for customly created RL…
The main features of facebookresearch/minihack are: Reinforcement Learning, World Models.
Projects with overlapping indexed features include: farama-foundation/minigrid — Simple and easily configurable grid world environments for reinforcement learning. farama-foundation/gymnasium-robotics. farama-foundation/d4rl — A collection of reference environments for offline reinforcement learning. ai4finance-foundation/finrl — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated… alessiodm/drl-zh — Welcome to drlzh.ai: a hands-on deep reinforcement learning course where you build the algorithms, not just read about… airlab-polimi/mushroom.