Hackathon Overview
Our AI hackathon brought together a diverse group of participants, who collaborated to develop a variety of impressive projects based on:
195
Participants
15
Teams
6
AI Applications
Winners and Finalists
This event has now ended, but you can still register for upcoming events on lablab.ai. We look forward to seeing you at the next one!
Checkout Upcoming Events →Submitted Concepts, Prototypes and Pitches
Submissions from the teams participating in the Reinforcement Learning OpenAI Gym event and making it to the end 👊
Application
RL Introductory Hackathon for envs Cartpole, Walker , Lunar Lander
Los patxatxos
Final
3 environments, everything is good
CartPole
Reinforcement Learning: CartPole, Lunar Lander and Bipedal Walker
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.
Ack-Ack Learn
Radio Frequency Project
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.
RadioFrequency
Cartpole, LunarLander, 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.
Voyager
Mesa-optimizer team's submission to the RL hackathon
Used A2C and DQN for Lunar Lander DQN for Cartpole TQC for Bipedal Walker
Mesa-optimizers