reinforcement learning

Balancing Performance and Safety in RL Safe Reinforcement Learning (RL) is a subset of RL that focuses on learning policies that not only maximize the long-term reward but also ensure reasonable system performance and/or respect safety constraints during the learning and/or deployment processes [1]. Safety is the opposite of risk, which refers to the stochastic nature of the environment. An optimal policy for long-term reward maximization may still perform poorly in some catastrophic situations due to inherent uncertainty....

Reinforcement learning (RL) is a powerful framework for training agents to maximize cumulative reward, but it typically assumes risk-neutrality. This can lead to suboptimal behavior in practical scenarios where the consequences of unfavorable outcomes can be detrimental. What is risk? Generally, risk might arise whenever there is uncertainty. In a financial situation, investment risk can be identified with uncertain monetary loss. In a safety-critical engineering system, risk is the undesirable detrimental outcome....

Setting up an ML envronment can be a tricky thing. Here’s what worked for me on how to set up the environment and keep track of experiments. Project Setup Directory Structure Cookiecuttet Computational Environment Pycharm virtualenv pip install the packages: [1] Unzip the downloaded mjpro150 into ~/.mujoco/mjpro150, and place the mjkey.txt file at ~/.mujoco/mjkey.txt. [2] Run pip3 install -U 'mujoco-py<1.50.2,>=1.50.1' [3] Remove ~/.mujoco/mjpro150/bin/libglfw.3.dylib [4] Run brew install llvm boost hdf5 glfw...