samsara-rl 0.0.3

Vectorized Reinforcement Learning Library

Samsara RL

A vectorized NumPy implementation of foundational reinforcement learning algorithms, following David Silver's RL lecture series. Built for clarity and learning — each algorithm maps directly to the equations in the lectures.

Applications of RL include robotic manipulation, LLM fine-tuning, financial portfolio management, and adaptive control systems.

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Table of Contents

1. Installation
2. Quick Start
3. Planning
   • MDP Structure
   • Policy Iteration
   • Value Iteration
4. Model-Free Prediction
   • Monte Carlo
   • TD Learning
5. Model-Free Control
   • SARSA
   • Q-Learning
6. Function Approximation

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Installation

pip install samsara-rl

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Quick Start

from samsara_rl.mdp.grid_world.grid_world_mdp import GridWorldMDP
from samsara_rl.planning.policy_iteration import PolicyIteration

mdp = GridWorldMDP()
pi = PolicyIteration(mdp)
policy = pi.find_optimal_policy()

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Planning

Planning algorithms assume full knowledge of environment dynamics (transition probabilities and reward function). While not "true RL" — agents never have access to dynamics in practice — planning provides the theoretical foundation all RL algorithms build on.

MDP Structure

MDPs are represented as NumPy arrays. The included GridWorldMDP implements the 4x4 grid world from David Silver's Lecture 3.

Attribute	Shape	Description
state_action_transition_matrix	(S, A, S')	T(s, a, s') — transition probabilities
reward_matrix	(S, A, S')	R(s, a, s') — reward for each transition

Policy Iteration

Alternates between evaluating the current policy using the Bellman expectation equation and improving it greedily until the policy stops changing.

PolicyIteration(mdp, bellman_tolerance)

Argument	Type	Default	Description
mdp	MDP		MDP instance to solve
bellman_tolerance	float	0.01	Convergence threshold for policy evaluation

find_optimal_policy(max_iter)

Argument	Type	Default	Description
max_iter	int	99	Maximum number of policy iteration steps

Value Iteration

Applies the Bellman optimality equation directly each iteration. Equivalent to policy iteration with k=1 evaluation steps per improvement. Policy is extracted once at convergence.

ValueIteration(mdp, bellman_tolerance)

Argument	Type	Default	Description
mdp	MDP		MDP instance to solve
bellman_tolerance	float	0.01	Convergence threshold for value iteration

Examples

from samsara_rl.mdp.grid_world.grid_world_mdp import GridWorldMDP
from samsara_rl.planning.policy_iteration import PolicyIteration
from samsara_rl.planning.value_iteration import ValueIteration

mdp = GridWorldMDP()

policy = PolicyIteration(mdp, bellman_tolerance=0.001).find_optimal_policy(max_iter=50)
policy = ValueIteration(mdp, bellman_tolerance=0.001).find_optimal_policy()

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Model-Free Prediction

Model-free methods learn value functions directly from experience (sampled episodes) without access to the MDP's transition or reward dynamics.

Monte Carlo

Monte Carlo prediction estimates Q(s, a) by averaging sampled returns. After each episode, the return G_t (discounted cumulative reward from time step t onward) is computed for every visited state-action pair, and the Q-table is updated using constant-alpha learning:

Q(s, a) ← Q(s, a) + α (G_t − Q(s, a))

This is an every-visit implementation — if the same (s, a) pair appears multiple times in an episode, each occurrence triggers an update. Both every-visit and first-visit MC converge to the true Q^π.

Returns are computed in a fully vectorized pass using a cumulative-sum trick that avoids the standard reverse loop over time steps.

MonteCarloPrediction(mdp, policy, alpha, gamma)

Argument	Type	Default	Description
mdp	MDP		MDP instance to sample episodes from
policy	array		Stochastic policy of shape (S, A)
alpha	float	0.01	Learning rate for incremental Q updates
gamma	float	0.9	Discount factor

evaluate(max_iter)

Argument	Type	Default	Description
max_iter	int	40000	Number of episodes to sample from

Example

from samsara_rl.mdp.grid_world.grid_world_mdp import GridWorldMDP
from samsara_rl.prediction.monte_carlo import MonteCarloPrediction

mdp = GridWorldMDP()
policy = mdp.random_policy()

mc = MonteCarloPrediction(mdp, policy, alpha=0.01, gamma=0.9)
mc.evaluate(max_iter=10000)

# V(s) for the random policy (expected value over actions)
v = mc.q_table.mean(axis=1).reshape(4, 4)

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Model-Free Control

TODO

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Function Approximation

TODO