Reinforcement Learning Agent

Overview

A comprehensive implementation of various reinforcement learning algorithms, from classic tabular methods to modern deep RL approaches. This project demonstrates the ability to train agents that learn optimal policies through interaction with their environment, solving both classic control problems and custom environments.

Implemented Algorithms

• Q-Learning: Classic temporal-difference learning for discrete state-action spaces
• Deep Q-Network (DQN): Value-based deep RL with experience replay and target networks
• Double DQN: Addressing overestimation bias in Q-learning
• A3C (Asynchronous Advantage Actor-Critic): Policy gradient method with parallel workers
• PPO (Proximal Policy Optimization): State-of-the-art policy gradient algorithm
• DDPG: Deep deterministic policy gradient for continuous control

Environments & Benchmarks

The agents have been trained and evaluated on various environments:

• Classic Control: CartPole, MountainCar, Pendulum
• Atari Games: Breakout, Space Invaders, Pong
• Continuous Control: LunarLander, BipedalWalker
• Custom Environments: Grid worlds, navigation tasks, resource management

Key Features

• Modular architecture allowing easy experimentation with different algorithms
• Experience replay buffer with prioritized sampling
• Tensorboard integration for training visualization
• Hyperparameter tuning with Optuna
• Parallel environment execution for faster training
• Model checkpointing and evaluation framework

Technical Highlights

The implementation focuses on both performance and code clarity:

• Neural Network Architectures: Custom CNN architectures for visual inputs, MLP for state vectors
• Training Optimizations: GPU acceleration, vectorized environments, efficient data pipelines
• Exploration Strategies: Epsilon-greedy, Boltzmann exploration, parameter noise
• Reward Engineering: Reward shaping and normalization techniques
• Stability Improvements: Gradient clipping, learning rate scheduling, normalization layers

Results & Performance

Notable achievements across different environments:

• CartPole: Consistent 500 episode reward (optimal) within 100 episodes
• LunarLander: Average reward of 250+ after 1500 episodes
• Atari Breakout: Human-level performance after 10M frames
• Custom navigation tasks: 95%+ success rate in complex scenarios

Visualizations & Analysis

• Training curves showing reward progression over time
• Q-value heatmaps for state-action spaces
• Policy visualization in continuous action spaces
• Episode replays with agent decision-making overlay

Future Work

• Implementation of model-based RL algorithms (World Models, Dreamer)
• Multi-agent reinforcement learning scenarios
• Inverse reinforcement learning for learning from demonstrations
• Transfer learning between related tasks
• Integration with real-world robotics platforms