⛰ Reinforcement learning model trying to make car reach to top of mountain
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Updated
Jun 1, 2024 - Python
⛰ Reinforcement learning model trying to make car reach to top of mountain
An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
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VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
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[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
Simple and easily configurable grid world environments for reinforcement learning
A grid-like environment (multi-agent system) used by an intelligent agent (or more than one agent) in order for it/them to carry the orbs to the pits in a limited number of movements.
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools.
Multi-Objective Reinforcement Learning algorithms implementations.
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Endpoints: /push-pull-legs , /upper-lower-ab , /upper-lower , /fbw-ab , /fbw-beginner , /bro-split.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
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