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Kotlin implementation of algorithms, examples, and exercises from theSutton and Barto: Reinforcement Learning (2nd Edition). The purpose of this project is to help understanding RL algorithms and experimenting easily.

Inspired byShangtongZhang/reinforcement-learning-an-introduction (Python)andidsc-frazzoli/subare (Java 8)

Features:

• Algorithms and problems are separated. So you can experiment with various combination of <algorithm, problem> or <algorithm,function approximator, problem>
• Implementation is very close to the pseudo code in the book. So reading source code will help you understand the original algorithm.

Model-based (Dynamic Programming):

• Policy Iteration (Action-Value Iteration) (p.65)
• Value Iteration (p.67)

Monte Carlo (episode backup):

• First-visit MC prediction (p.76)
• Monte Carlo Exploring Starts (p.81)
• On-Policy first-visit MC control (p.83)
• Off-policy MC prediction (p.90)
• Off-policy MC control (p.91)

Temporal Difference (one-step backup):

• Tabular TD(0) (p.98)
• Sarsa (p.106)
• Q-learning (p.107)
• Expected Sarsa (p.109)
• Double Q-Learning (p.111)

n-step Temporal Difference (unify MC and TD):

• n-step TD prediction (p.117)
• n-step Sarsa (p.120)
• Off-policy n-step Sarsa (p.122)
• n-step Tree Backup (p.125)
• Off-policy n-step Q(σ) (p.128)

Dyna (Integrate Planning, Acting, and Learning):

• Random-sample one-step tabular Q-planning (p.133)
• Tabular Dyna-Q (p.135)
• Tabular Dyna-Q+ (p.138)
• Prioritized Sweeping (p.140)
• Prioritized Sweeping Stochastic Environment (p.141)

On-policy Prediction with Function Approximation

• Gradient Monte Carlo algorithm (p.165)
• Semi-gradient TD(0) (p.166)
• n-step semi-gradient TD (p.171)
• Least-Squares TD (p.186)

On-policy Control with Function Approximation

• Episodic semi-gradient Sarsa (p.198)
• Episodic semi-gradient n-step Sarsa (p.200)
• Differential semi-gradient Sarsa (p.203)
• Differential semi-gradient n-step Sarsa (p.206)

Off-policy Methods with Approximation

• Semi-gradient off-policy TD(0) (p.210)
• Semi-gradient Expected Sarsa (p.210)
• n-step semi-gradient off-policy Sarsa (p.211)
• n-step semi-gradient off-policy Q(σ) (p.211)

Eligibility Traces

• Off-line λ-return (p.238)
• Semi-gradient TD(λ) prediction (p.240)
• True Online TD(λ) prediction (p.246)
• Sarsa(λ) (p.250)
• True Online Sarsa(λ) (p.252)

Policy Gradient Methods

• REINFORCE, A Monte-Carlo Policy-Gradient Method (episodic) (p.271)
• REINFORCE with Baseline (episodic) (p.273)
• One-step Actor-Critic (episodic) (p.274)
• Actor-Critic with Eligibility Traces (episodic) (p.275)
• Actor-Critic with Eligibility Traces (continuing) (p.277)

• Grid world (p.61)
• Jack's Car Rental and exercise 4.4 (p.65)
• Gambler's Problem (p.68)
• Blackjack (p.76)
• Random Walk (p.102)
• Windy Gridworld and King's Moves (p.106)
• Cliff Walking (p.108)
• Maximization Bias Example (p.110)
• 19-state Random Walk (p.118)
• Dyna Maze (p.136)
• Rod Maneuvering (p.141)
• 1000-state Random Walk (p.166)
• Mountain Car (p.198)
• Access-Control Queuing Task (p.204)

Built withMaven

TryTestcases

Figure 7.2: Performance of n-step TD methods as acc function of α, for various values of n, on acc 19-state randomwalk task

Figure 10.1: The Mountain Car task and the cost-to-go function learnedduring one run

Figure 10.4: Effect of the α and n on early performance of n-step semi-gradient Sarsa and tile-coding functionapproximation on the Mountain Car task

Figure 12.3: 19-state Random walk results: Performance of the offline λ-return algorithm .

Figure 12.6: 19-state Random walk results: Performance of TD(λ) .

Figure 12.8: 19-state Random walk results: Performance of online λ-return algorithms

Figure 12.10: Early performance on the Mountain Car task of Sarsa(λ) with replacing traces

Figure 12.11: Summary comparison of Sarsa(λ) algorithms on the Mountain Car task.