You can read more: https://en.wikipedia.org/wiki/Reinforcement_learning
Solutions built with Reinforcement Learning that have been created during our hackathons by the members of our community
Used A2C and DQN for Lunar Lander DQN for Cartpole TQC for Bipedal Walker
Applied reinforcement learning for agent to play these 3 games: Cartpole, LunarLander, BiPedal Walker. We used the basic model of Environment -> State -> Agent -> Action to train our agent. We reward the agent for achieving an outcome that we want, while penalizing the agent for doing otherwise. After many iterations, our agents learns to clear the games.
We have completed 2 challenges. The first one (cartpole) was completed using our own code, we implemented Deep Q Learning. For the second one (Lunar Lander) we used stable_baseline library.
Using the stable_baselines3 library, we tried to solve the problems proposed in the challenge. We used a Proximal Policy Optimization (PPO) Model. The Policy we used is a standard MLP. We tried to change the number of iteration to achieve a better performance.
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