In my previous LunarLander-v2 and BipedalWalker-v3 tutorials, I was gathering experience in writing Proximal Policy Optimization algorithms, to get some background to start developing my own RL environment. The mentioned tutorials were written upon OpenAI's gym package, which allows us to experiment with our own reinforcement learning algorithms upon a ton of free environments to experiment with.
These OpenAI environments are great for learning, but eventually, we will want to set up an agent to solve the custom problem. To do this, we would need to create a custom environment, specific to our problem domain. When we have our custom environment, we can create a custom Reinforcement Learning agent to simulate crypto trades. This is the first part of these tutorials, so this and the rest of the tutorials related to this article will be available on my GitHub page.
Text version tutorial: RL-BTC-BOT-backbone/
GitHub code: pythonlessons/RL-Bitcoin-trading-bot/tree/main/RL-Bitcoin-trading-bot_1
✅ Support My Channel Through Patreon:
PyLessons
✅ One-Time Contribution Through PayPal:
paypalme/PyLessons
These OpenAI environments are great for learning, but eventually, we will want to set up an agent to solve the custom problem. To do this, we would need to create a custom environment, specific to our problem domain. When we have our custom environment, we can create a custom Reinforcement Learning agent to simulate crypto trades. This is the first part of these tutorials, so this and the rest of the tutorials related to this article will be available on my GitHub page.
Text version tutorial: RL-BTC-BOT-backbone/
GitHub code: pythonlessons/RL-Bitcoin-trading-bot/tree/main/RL-Bitcoin-trading-bot_1
✅ Support My Channel Through Patreon:
PyLessons
✅ One-Time Contribution Through PayPal:
paypalme/PyLessons
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