Continuous Control with TD3 (Twin Delayed DDPG) on BipedalWalker-v3

Implemented a reinforcement learning agent to master continuous walking dynamics in OpenAI Gym’s BipedalWalker-v3 environment.

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Objective

To explore continuous control and policy gradient optimization through TD3.

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Methodology

1. Environment
   • BipedalWalker-v3 from OpenAI Gym.
2. Algorithm
   • Actor-Critic network architecture with:
     • Twin Q-Networks
     • Delayed policy updates
     • Target policy smoothing
3. Training
   • Replay buffer, target network updates, and Ornstein-Uhlenbeck noise.
4. Performance
   • Achieved average rewards >300 after 100 episodes.